426 APPENDIX A

▻ z 0 1 2 3 4 5 6 7 8 9
2.0 0.9772 0.9778 0.9783 0.9788 0.9793 0.9798 0.9803 0.9808 0.9812 0.9817
2.1 0.9821 0.9826 0.9830 0.9834 0.9838 0.9842 0.9846 0.9850 0.9854 0.9857
2.2 0.9861 0.9864 0.9868 0.9871 0.9875 0.9878 0.9881 0.9884 0.9887 0.9890
2.3 0.9893 0.9896 0.9898 0.9901 0.9904 0.9906 0.9909 0.9911 0.9913 0.9916
2.4 0.9918 0.9920 0.9922 0.9925 0.9927 0.9929 0.9931 0.9932 0.9934 0.9936
2.5 0.9938 0.9940 0.9941 0.9943 0.9945 0.9946 0.9948 0.9949 0.9951 0.9952
2.6 0.9953 0.9955 0.9956 0.9957 0.9959 0.9960 0.9961 0.9962 0.9963 0.9964
2.7 0.9965 0.9966 0.9967 0.9968 0.9969 0.9970 0.9971 0.9972 0.9973 0.9974
2.8 0.9974 0.9975 0.9976 0.9977 0.9977 0.9978 0.9979 0.9979 0.9980 0.9981
2.9 0.9981 0.9982 0.9982 0.9983 0.9984 0.9984 0.9985 0.9985 0.9986 0.9986
3.0 0.9987 0.9987 0.9987 0.9988 0.9988 0.9989 0.9989 0.9989 0.9990 0.9990
Examples: If Z ~ Normal(0,1), then P(Z ≤ −1.32) = .0934 and P(Z ≤ 1.84) = .9671.

Source: This table was generated using the Stata® function normprob.

TABLE A.2 CRITICAL VALUES OF THE t DISTRIBUTION

Significance level
1-Tailed: .10 .05 .025 .01 .005
2-Tailed: .20 .10 .05 .02 .01
Degrees of 1 3.078 6.314 12.706 31.821 63.657
freedom 2 1.886 2.920 4.303 6.965 9.925
3 1.638 2.353 3.182 4.541 5.841
4 1.533 2.132 2.776 3.747 4.604
5 1.476 2.015 2.571 3.365 4.032
6 1.440 1.943 2.447 3.143 3.707
7 1.415 1.895 2.365 2.998 3.499
8 1.397 1.860 2.306 2.896 3.355
9 1.383 1.833 2.262 2.821 3.250
10 1.372 1.812 2.228 2.764 3.169
11 1.363 1.796 2.201 2.718 3.106
12 1.356 1.782 2.179 2.681 3.055
13 1.350 1.771 2.160 2.650 3.012
14 1.345 1.761 2.145 2.624 2.977
15 1.341 1.753 2.131 2.602 2.947
16 1.337 1.746 2.120 2.583 2.921
17 1.333 1.740 2.110 2.567 2.898
18 1.330 1.734 2.101 2.552 2.878
19 1.328 1.729 2.093 2.539 2.861
20 1.325 1.725 2.086 2.528 2.845
21 1.323 1.721 2.080 2.518 2.831
22 1.321 1.717 2.074 2.508 2.819
23 1.319 1.714 2.069 2.500 2.807
24 1.318 1.711 2.064 2.492 2.797
25 1.316 1.708 2.060 2.485 2.787
26 1.315 1.706 2.056 2.479 2.779
27 1.314 1.703 2.052 2.473 2.771
28 1.313 1.701 2.048 2.467 2.763
29 1.311 1.699 2.045 2.462 2.756
30 1.310 1.697 2.042 2.457 2.750
40 1.303 1.684 2.021 2.423 2.704
60 1.296 1.671 2.000 2.390 2.660
90 1.291 1.662 1.987 2.368 2.632
120 1.289 1.658 1.980 2.358 2.617
∞ 1.282 1.645 1.960 2.326 2.576
Examples: The 1% critical value for a one-tailed test with 25 df is 2.485. The 5% critical value for a two-tailed test with large (> 120) df is 1.96.

Source: This table was generated using the Stata® function invttail.
 STATISTICAL TABLES 427

TABLE A.3A 10% CRITICAL VALUES OF THE F DISTRIBUTION

Numerator degrees of freedom
1 2 3 4 5 6 7 8 9 10
Denominator 10 3.29 2.92 2.73 2.61 2.52 2.46 2.41 2.38 2.35 2.32
degrees of 11 3.23 2.86 2.66 2.54 2.45 2.39 2.34 2.30 2.27 2.25
freedom 12 3.18 2.81 2.61 2.48 2.39 2.33 2.28 2.24 2.21 2.19
13 3.14 2.76 2.56 2.43 2.35 2.28 2.23 2.20 2.16 2.14
14 3.10 2.73 2.52 2.39 2.31 2.24 2.19 2.15 2.12 2.10
15 3.07 2.70 2.49 2.36 2.27 2.21 2.16 2.12 2.09 2.06
16 3.05 2.67 2.46 2.33 2.24 2.18 2.13 2.09 2.06 2.03
17 3.03 2.64 2.44 2.31 2.22 2.15 2.10 2.06 2.03 2.00
18 3.01 2.62 2.42 2.29 2.20 2.13 2.08 2.04 2.00 1.98
19 2.99 2.61 2.40 2.27 2.18 2.11 2.06 2.02 1.98 1.96
20 2.97 2.59 2.38 2.25 2.16 2.09 2.04 2.00 1.96 1.94
21 2.96 2.57 2.36 2.23 2.14 2.08 2.02 1.98 1.95 1.92
22 2.95 2.56 2.35 2.22 2.13 2.06 2.01 1.97 1.93 1.90
23 2.94 2.55 2.34 2.21 2.11 2.05 1.99 1.95 1.92 1.89
24 2.93 2.54 2.33 2.19 2.10 2.04 1.98 1.94 1.91 1.88
25 2.92 2.53 2.32 2.18 2.09 2.02 1.97 1.93 1.89 1.87
26 2.91 2.52 2.31 2.17 2.08 2.01 1.96 1.92 1.88 1.86
27 2.90 2.51 2.30 2.17 2.07 2.00 1.95 1.91 1.87 1.85
28 2.89 2.50 2.29 2.16 2.06 2.00 1.94 1.90 1.87 1.84
29 2.89 2.50 2.28 2.15 2.06 1.99 1.93 1.89 1.86 1.83
30 2.88 2.49 2.28 2.14 2.05 1.98 1.93 1.88 1.85 1.82
40 2.84 2.44 2.23 2.09 2.00 1.93 1.87 1.83 1.79 1.76
60 2.79 2.39 2.18 2.04 1.95 1.87 1.82 1.77 1.74 1.71
90 2.76 2.36 2.15 2.01 1.91 1.84 1.78 1.74 1.70 1.67
120 2.75 2.35 2.13 1.99 1.90 1.82 1.77 1.72 1.68 1.65
∞ 2.71 2.30 2.08 1.94 1.85 1.77 1.72 1.67 1.63 1.60
Example: The 10% critical value for numerator df = 2 and denominator df = 40 is 2.44.

Source: This table was generated using the Stata® function invFtail.

TABLE A.3B 5% CRITICAL VALUES OF THE F DISTRIBUTION

Numerator degrees of freedom
1 2 3 4 5 6 7 8 9 10
Denominator 10 4.96 4.10 3.71 3.48 3.33 3.22 3.14 3.07 3.02 2.98
degrees of 11 4.84 3.98 3.59 3.36 3.20 3.09 3.01 2.95 2.90 2.85
freedom 12 4.75 3.89 3.49 3.26 3.11 3.00 2.91 2.85 2.80 2.75
13 4.67 3.81 3.41 3.18 3.03 2.92 2.83 2.77 2.71 2.67
14 4.60 3.74 3.34 3.11 2.96 2.85 2.76 2.70 2.65 2.60
15 4.54 3.68 3.29 3.06 2.90 2.79 2.71 2.64 2.59 2.54
16 4.49 3.63 3.24 3.01 2.85 2.74 2.66 2.59 2.54 2.49
17 4.45 3.59 3.20 2.96 2.81 2.70 2.61 2.55 2.49 2.45
18 4.41 3.55 3.16 2.93 2.77 2.66 2.58 2.51 2.46 2.41
19 4.38 3.52 3.13 2.90 2.74 2.63 2.54 2.48 2.42 2.38
20 4.35 3.49 3.10 2.87 2.71 2.60 2.51 2.45 2.39 2.35
21 4.32 3.47 3.07 2.84 2.68 2.57 2.49 2.42 2.37 2.32
22 4.30 3.44 3.05 2.82 2.66 2.55 2.46 2.40 2.34 2.30
23 4.28 3.42 3.03 2.80 2.64 2.53 2.44 2.37 2.32 2.27
24 4.26 3.40 3.01 2.78 2.62 2.51 2.42 2.36 2.30 2.25
25 4.24 3.39 2.99 2.76 2.60 2.49 2.40 2.34 2.28 2.24
26 4.23 3.37 2.98 2.74 2.59 2.47 2.39 2.32 2.27 2.22
27 4.21 3.35 2.96 2.73 2.57 2.46 2.37 2.31 2.25 2.20
28 4.20 3.34 2.95 2.71 2.56 2.45 2.36 2.29 2.24 2.19
29 4.18 3.33 2.93 2.70 2.55 2.43 2.35 2.28 2.22 2.18

▻
428 APPENDIX A

▻ Numerator degrees of freedom

1 2 3 4 5 6 7 8 9 10
30 4.17 3.32 2.92 2.69 2.53 2.42 2.33 2.27 2.21 2.16
40 4.08 3.23 2.84 2.61 2.45 2.34 2.25 2.18 2.12 2.08
60 4.00 3.15 2.76 2.53 2.37 2.25 2.17 2.10 2.04 1.99
90 3.95 3.10 2.71 2.47 2.32 2.20 2.11 2.04 1.99 1.94
120 3.92 3.07 2.68 2.45 2.29 2.17 2.09 2.02 1.96 1.91
∞ 3.84 3.00 2.60 2.37 2.21 2.10 2.01 1.94 1.88 1.83
Example: The 5% critical value for numerator df = 4 and large denominator df (∞) is 2.37.

Source: This table was generated using the Stata® function invFtail.

TABLE A.3C 1% CRITICAL VALUES OF THE F DISTRIBUTION

Numerator degrees of freedom
1 2 3 4 5 6 7 8 9 10
Denominator 10 10.04 7.56 6.55 5.99 5.64 5.39 5.20 5.06 4.94 4.85
degrees of 11 9.65 7.21 6.22 5.67 5.32 5.07 4.89 4.74 4.63 4.54
freedom 12 9.33 6.93 5.95 5.41 5.06 4.82 4.64 4.50 4.39 4.30
13 9.07 6.70 5.74 5.21 4.86 4.62 4.44 4.30 4.19 4.10
14 8.86 6.51 5.56 5.04 4.69 4.46 4.28 4.14 4.03 3.94
15 8.68 6.36 5.42 4.89 4.56 4.32 4.14 4.00 3.89 3.80
16 8.53 6.23 5.29 4.77 4.44 4.20 4.03 3.89 3.78 3.69
17 8.40 6.11 5.18 4.67 4.34 4.10 3.93 3.79 3.68 3.59
18 8.29 6.01 5.09 4.58 4.25 4.01 3.84 3.71 3.60 3.51
19 8.18 5.93 5.01 4.50 4.17 3.94 3.77 3.63 3.52 3.43
20 8.10 5.85 4.94 4.43 4.10 3.87 3.70 3.56 3.46 3.37
21 8.02 5.78 4.87 4.37 4.04 3.81 3.64 3.51 3.40 3.31
22 7.95 5.72 4.82 4.31 3.99 3.76 3.59 3.45 3.35 3.26
23 7.88 5.66 4.76 4.26 3.94 3.71 3.54 3.41 3.30 3.21
24 7.82 5.61 4.72 4.22 3.90 3.67 3.50 3.36 3.26 3.17
25 7.77 5.57 4.68 4.18 3.85 3.63 3.46 3.32 3.22 3.13
26 7.72 5.53 4.64 4.14 3.82 3.59 3.42 3.29 3.18 3.09
27 7.68 5.49 4.60 4.11 3.78 3.56 3.39 3.26 3.15 3.06
28 7.64 5.45 4.57 4.07 3.75 3.53 3.36 3.23 3.12 3.03
29 7.60 5.42 4.54 4.04 3.73 3.50 3.33 3.20 3.09 3.00
30 7.56 5.39 4.51 4.02 3.70 3.47 3.30 3.17 3.07 2.98
40 7.31 5.18 4.31 3.83 3.51 3.29 3.12 2.99 2.89 2.80
60 7.08 4.98 4.13 3.65 3.34 3.12 2.95 2.82 2.72 2.63
90 6.93 4.85 4.01 3.54 3.23 3.01 2.84 2.72 2.61 2.52
120 6.85 4.79 3.95 3.48 3.17 2.96 2.79 2.66 2.56 2.47
∞ 6.63 4.61 3.78 3.32 3.02 2.80 2.64 2.51 2.41 2.32
Example: The 1% critical value for numerator df = 3 and denominator df = 60 is 4.13.

Source: This table was generated using the Stata® function invFtail.
 lOMoARcPSD|14898814

Quiz Bank Economic and Financial Modelling

Economic and Financial Modelling (Western Sydney University)

Studocu is not sponsored or endorsed by any college or university

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 lOMoARcPSD|14898814

Which of the following statements is true?

a. Economic significance represents empirical appeal and meaning of the

statistical results.

b. We obtain economic significance by selecting a high significance level and

maximising the power of a test.

c. Statistical significance refers to what we derive as the estimates, but is not a

methodological outcome.

d. For statistical significance, we are interested to see if the relationships entailed by

the parameter of interest imply non-trivial empirical meaning.

A multiple regression model suffers from functional form

misspecification when it does not properly account for the
relationship between the dependent and the observed explanatory

True

An explanatory variable is called exogenous if it is correlated with

the error term.

False

The Least Absolute Deviations (LAD) estimators in a linear model

minimize the sum of squared residuals.

False

Studentized residuals are obtained from the original OLS residuals

by dividing them by an estimate of their standard deviation.

True

Under the classical errors-in-variables (CEV) assumption, the

measurement error means that the estimated OLS effect, on
average, is closer to zero than the true effect.

True

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 lOMoARcPSD|14898814

Which of the following is a test of nonnested models?

a. Standard F test
b. Regression Specification Error Test
c. Davidson-MacKinnon test
d. White test

A measurement error occurs in a regression model when _____.

a. the partial effect of an independent variable depends on unobserved factors

b. the dependent variable is binary
c. the observed value of a variable used in the model differs from its actual
value
d. the model includes more than two independent variables

Which of the following types of sampling always causes bias or

inconsistency in the ordinary least squares estimators?

a. Exogenous sampling
b. Endogenous sampling
c. Random sampling
d. Stratified sampling

How many new variables should be created for a

multiple regression model where data are always
available for y and x1, x2, ..., xk−1 but are sometimes
missing for the explanatory variable xk?

a. Four variables
b. Three variables
c. One variable
d. Two variables

Which of the following is a drawback of including proxy variables in

a regression model?

a. It reduces the error variance.

b. It exacerbates multicollinearity.
c. It leads to misspecification analysis.
d. It increases the error variance.

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 lOMoARcPSD|14898814

A complete cases estimator is an estimator that uses:

a. complete information about the residuals.

b. complete information about the outliers.
c. only observations with a complete set of data on x1, ..., xk.
d. only observations with a complete set of data on y and x1, ..., xk.

Which of the following is true of Regression Specification Error

Test (RESET)?

a. It detects the presence of dummy variables in a regression model.

b. It tests if the functional form of a regression model is misspecified.
c. It helps in the detection of multicollinearity among the independent variables in a
regression model.
d. It helps in the detection of heteroskedasticity when the functional form of the
model is correctly specified.

A regression model suffers from functional form misspecification if

_____.

a. the dependent variable is binary.

b. the coefficient of a key variable is zero.
c. a key variable is binary.
d. an interaction term is omitted.

Which of the following is a difference between least absolute

deviations (LAD) and ordinary least squares (OLS) estimation?

a. OLS is more computationally intensive than LAD.

b. OLS is more sensitive to outlying observations than LAD.
c. OLS is designed to estimate the conditional median of the dependent variable
while LAD is designed to estimate the conditional mean.
d. OLS is justified for very large sample sizes while LAD is justified for smaller
sample sizes.

Which of the following assumptions is needed for the plug-in

solution to the omitted variables problem to provide consistent
estimators?

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 lOMoARcPSD|14898814

a. The error term in the regression model exhibits heteroskedasticity.

b. The error term in the regression model is uncorrelated with all the
independent variables.
c. The proxy variable is uncorrelated with the dependent variable.
d. The proxy variable has zero conditional mean.

Sample selection based on the dependent variable is called _____.

a. exogenous sample selection

b. endogenous sample selection
c. stratified sample selection
d. random sample selection

If and B1 and B2 are estimated values of regression coefficients

associated with two explanatory variables in a regression equation,
then the standard error (B1-B2 ) = standard error ( B1) – standard
error ( B2).

False

The ordinary least square estimators have the smallest variance

among all the unbiased estimators.

True

Standard errors must always be positive.

True

H 1 : â j 0, where â j is a regression coefficient associated with an

explanatory variable, represents a one-sided alternative
hypothesis.

False

Suppose that you are interested in estimating the average impact a

job training program has on wages. After controlling for observed
factors that influence wages, participation in the training program,
you find that the coefficient for the training is 0.55 and the standard

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 lOMoARcPSD|14898814

error is 1.06. Thus, we can infer that the training program has a
positive and statistically significant impact on wages at a 95%
confidence level.

False

Suppose that in order to estimate of the average effect of

participation in a job training program (train) on wage, you included
controls for education (educ), years of experience (exper) in your
model: log(wage) = + + + + u . Which of the following is a reason
why the coefficient, , might be biased?

a. Educ is likely correlated with both wage and train .

b. Educ and exper are highly correlated with each other.
c. There might be some other variable that is also correlated with both wage
and train.
d. Exper is uncorrelated with train.

Which of the following 95% confidence intervals for the estimate of

the population treatment effect is the most convincing evidence
that a policy intervention was indeed effective?

a. (-0.2, 0.3).
b. (-0.1, 0.5).
c. (0.5, 0.75).
d. (-0.5, 0.9).

Which of the following tools is used to test multiple linear

restrictions?

a. z test
b. t test
c. F test
d. Unit root test

Which of the following is true of confidence intervals?

a. Two quantities and c are required to construct the confidence intervals.

b. A constructed confidence intervals will always contain the population parameter .
c. To obtain the value c in the confidence interval, only the degrees of freedom need

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 lOMoARcPSD|14898814

to be known.
d. Confidence intervals are also called interval estimates.

What is a type I error?

a. Rejecting H0 when it is false

b. Failure to reject H0 when it is actually false
c. Rejecting H0 when it is true
d. Failure to reject H0 when it is actually true

The key assumption for the general multiple regression model is

that all factors in the unobserved error term be correlated with the
explanatory variables.

False

If two regressions use different sets of observations, then we can

tell how the R-squareds will compare, even if one regression uses a
subset of regressors.

False

The term “linear” in a multiple linear regression model means that

the equation is linear in parameters.

True

In a multiple linear regression model, , where is a binary variable

and is the years of experience, is the difference in the average
wage between males and non-males, after accounting for
experience.

True

A larger error variance makes it difficult to estimate the partial

effect of any of the independent variables on the dependent
variable.

True

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A discrete random variable is one that takes on only a finite number

of values.

True

In general, what do small p-values indicate?

a. Low statistical power

b. Evidence against H0
c. Low significance level
d. Evidence for H0

A _____ variable is used to incorporate qualitative information in a

regression model.

a. dummy
b. continuous
c. dependent
d. binomial

Which of the following is true of dependent variables?

a. A dependent variable can only have a numerical value.

b. A dependent variable cannot have a qualitative meaning.
c. A dependent variable can be binary.
d. A dependent variable cannot have more than 2 values.

The sum of squared residuals form of the F statistic can be

computed easily even when many independent variables are
involved; this particular F statistic is usually called the _____ in
econometrics

a. LM statistic
b. Chow statistic
c. statistic
d. t statistic

Which of the following is true of the Chow test?

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a. It is a type of t test.
b. It is only valid under homoskedasticty.
c. It is only valid under heteroskedasticity.
d. It is a type of sign test.

What is a type II error?

a. Rejecting H0 when it is false

b. Rejecting H0 when it is true
c. Failure to reject H0 when it is actually true
d. Failure to reject H0 when it is actually false

The sum of squared residuals form of the F statistic can be

computed easily even when many independent variables are
involved; this particular F statistic is usually called the _____ in
econometrics.

a. t statistic
b. Chow statistic
c. statistic
d. LM statistic

Which of the following Gauss -Markov assumptions is violated by

the linear probability model?

a. The assumption that none of the independent variables are constants.

b. The assumption of constant variance of the error term.
c. The assumption of no exact linear relationship among independent variables.
d. The assumption of zero conditional mean of the error term.

A binary response is the most extreme form of a discrete random

variable that takes on:

a. any value.
b. only one value, one.
c. only two values, zero and one.
d. only one value, zero.

Which of the following is true of dependent variables?

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a. A dependent variable can be binary.

b. A dependent variable can only have a numerical value.
c. A dependent variable cannot have a qualitative meaning.
d. A dependent variable cannot have more than 2 values.

In a self-selection problem, the explanatory variables can be:

a. endogenous.
b. independent.
c. exogenous.
d. random.

A binary variable is a variable whose value changes with a change

in the number of observations.

False

If the p-value of an F statistic 2.63 is 0.034, then we can say that the
problem of interest is significant at the 5% level.

True

The dummy variable coefficient for a particular group represents

the estimated difference in intercepts between that group and the
base group.

True

A dummy variable trap arises when a single dummy variable

describes a given number of groups.

False

If OLS estimators satisfy asymptotic normality, it implies that:

a. they are approximately normally distributed in samples with less than 10

observations.
b. they have a constant mean equal to zero and variance equal to 2.
c. they have a constant mean equal to one and variance equal to .
d. they are approximately normally distributed in large enough sample sizes.

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When the error term is not normally distributed, then is sometimes

called the:

a. asymptotic standard error.

b. asymptotic t statistic.
c. asymptotic normality.
d. asymptotic confidence interval.

If j is an OLS estimator of a regression coefficient associated with

one of the explanatory variables, such that j = 1, 2, …., n,
asymptotic standard error of j will refer to the:

a. square root of the estimated variance of j when the error term is not
normally distributed.
b. estimated variance of j when the error term is normally distributed.
c. square root of the estimated variance of j when the error term is normally
distributed.
d. estimated variance of a given coefficient when the error term is not normally
distributed.

If the error term is correlated with any of the independent variables,

the OLS estimators are:

a. unbiased and consistent.

b. biased and inconsistent.
c. biased and consistent.
d. unbiased and inconsistent.

An auxiliary regression refers to a regression that is used:

a. when the independent variables are qualitative in nature.

b. when the dependent variables are qualitative in nature.
c. to compute a test statistic but whose coefficients are not of direct interest.
d. to compute coefficients which are of direct interest in the analysis.

A p-value indicates the incompatibility of the data with a specified

statistical model and is not a measure of the size of an effect or the
probability that the studied hypothesis is true.

True

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Even if the error terms in a regression equation, u 1, u 2, …, un, are

not normally distributed, the estimated coefficients can be normally
distributed.

False

The F statistic is also referred to as the score statistic.

False

The LM statistic requires estimation of the unrestricted model only.

False

In the multiple regression model , if x1 is correlated with u but the

other independent variables are uncorrelated with u, then all of the
OLS estimators are generally consistent

False

If Cov(z,x) ≠ 0, then z and x are correlated.

True

A binary variable is a variable whose value changes with a change

in the number of observations.

False

If the p-value of an F statistic 2.63 is 0.034, then we can say that the
problem of interest is significant at the 5% level.

True

The parameters in a linear probability model can be interpreted as

measuring the change in the probability that y = 1 due to a one-unit
increase in an explanatory variable.

True

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The multiple linear regression model with a binary dependent

variable is called the linear probability model.

True

The assumption that there are no exact linear relationships among

the independent variables in a multiple linear regression model
fails if _____, where n is the sample size and k is the number of
parameters.

a. n > 2
b. n = k + 1
c. n < k + 1
d. n > k

ignores the error variance increase because it treats both

regressors as _____.

a. dependent
b. nonrandom
c. random
d. independent

In the equation, is a(n) _____.

a. independent variable
b. dependent variable
c. slope parameter
d. intercept parameter

If an independent variable in a multiple linear regression model is

an exact linear combination of other independent variables, the
model suffers from the problem of _____.

a. homoskedasticity
b. perfect collinearity
c. omitted variable bias
d. heteroskedasticty

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Which of the following is true of BLUE?

a. It is the best linear uniform estimator.

b. An estimator is linear if and only if it can be expressed as a linear function
of the data on the dependent variable.
c. An estimator is an unbiased estimator of if for any .
d. It is a rule that can be applied to any one value of the data to produce an estimate.

High (but not perfect) correlation between two or more independent

variables is called _____.

a. heteroskedasticty
b. micronumerosity
c. homoskedasticty
d. multicollinearity

Which of the following often implies that a single variable acts as a ‘sufficient
statistic’ for predicting the outcome variable, y?

a. Conditional independence assumption

b. Efficient markets theories
c. Ceteris paribus
d. Gauss-Markov theorem

In econometrics, the general partialling out result is usually called

the _____.

a. Frisch-Waugh theorem
b. Best linear unbiased estimator
c. Gauss-Markov theorem
d. Gauss-Markov assumption

Which of the following is true of R 2?

a. R2 usually decreases with an increase in the number of independent variables in

a regression.
b. A low R2 indicates that the Ordinary Least Squares line fits the data well.
c. R2 is also called the standard error of regression.
d. R2 shows what percentage of the total variation in the dependent variable, Y,
is explained by the explanatory variables.

The Cauchy-Schwartz inequality implies that the asymptotic

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variance of is:

a. less than or equal to .

b. greater than .
c. less than .
d. equal to .

The n- R-squared statistic also refers to the:

a. z statistic.
b. t statistic.
c. F statistic.
d. LM statistic.

If OLS estimators satisfy asymptotic normality, it implies that:

a. they have a constant mean equal to one and variance equal to .

b. they are approximately normally distributed in samples with less than 10
observations.
c. they are approximately normally distributed in large enough sample sizes.
d. they have a constant mean equal to zero and variance equal to 2.

If j is an OLS estimator of a regression coefficient associated with

one of the explanatory variables, such that j = 1, 2, …., n,
asymptotic standard error of j will refer to the:

a. square root of the estimated variance of j when the error term is normally
distributed.
b. square root of the estimated variance of j when the error term is not
normally distributed.
c. estimated variance of j when the error term is normally distributed.
d. estimated variance of a given coefficient when the error term is not normally
distributed.

A normally distributed random variable is symmetrically distributed

about its mean, it can take on any positive or negative value (but
with zero probability), and more than 95% of the area under the
distribution is within two standard deviations.

True

If variance of an independent variable in a regression model, say x

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1, is greater than 0, or Var( x 1) > 0, the inconsistency in 1

(estimator associated with x 1) is negative, if x 1 and the error term
are positively related.

False

What is the rejection rule for a positive one-tail hypothesis test?

a. t > c
b. |t| < c
c. |t| > c
d. t < c

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Quiz-TH done đến chap 10

Econometrics (Trường Đại học Kinh tế Thành phố Hồ Chí Minh)

Studocu is not sponsored or endorsed by any college or university

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Chapter 1
1. Econometrics is the branch of economics that _____.
a. studies the behavior of individual economic agents in making economic decisions
b. develops and uses statistical methods for estimating economic
relationships
c. deals with the performance, structure, behavior, and decision-making of an
economy as a whole
d. applies mathematical methods to represent economic theories and solve
economic problems.
2. Nonexperimental data is called _____.
a. cross-sectional data
b. time series data
c. observational data
d. panel data
3. Which of the following is true of experimental data?
a. Experimental data are collected in laboratory environments in the natural
sciences.
b. Experimental data cannot be collected in a controlled environment.
c. Experimental data is sometimes called observational data.
d. Experimental data is sometimes called retrospective data.
4. An empirical analysis relies on _____to test a theory.
a. common sense
b. ethical considerations
c. data
d. customs and conventions
5. The term ‘u’ in an econometric model is usually referred to as the _____.
a. error term
b. parameter
c. hypothesis
d. dependent variable
6. The parameters of an econometric model _____.
a. include all unobserved factors affecting the variable being studied
b. describe the strength of the relationship between the variable under study
and
the factors affecting it
c. refer to the explanatory variables included in the model
d. refer to the predictions that can be made using the model
7. Which of the following is the first step in empirical economic analysis?
a. Collection of data
b. Statement of hypotheses
c. Specification of an econometric model
d. Testing of hypotheses
8. A data set that consists of a sample of individuals, households, firms, cities,
states, countries, or a variety of other units, taken at a given point in time, is
called a(n) _____.

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a. cross-sectional data set

b. longitudinal data set
c. time series data set
d. experimental data set
9. Data on the income of law graduates collected at different times during the
same year is_____.
a. panel data
b. experimental data
c. time series data
d. cross-sectional data
10. A data set that consists of observations on a variable or several variables
over time is called a _____ data set.
a. binary
b. cross-sectional
c. time series
d. experimental
11. Which of the following is an example of time series data?
a. Data on the unemployment rates in different parts of a country during a year.
b. Data on the consumption of wheat by 200 households during a year.
c. Data on the gross domestic product of a country over a period of 10 years.
d. Data on the number of vacancies in various departments of an organization on a
particular month.
12. Which of the following refers to panel data?
a. Data on the unemployment rate in a country over a 5-year period
b. Data on the birth rate, death rate and population growth rate in developing
countries over a 10-year period.
c. Data on the income of 5 members of a family on a particular year.
d. Data on the price of a company’s share during a year.
13. Which of the following is a difference between panel and pooled cross-
sectional data?
a. A panel data set consists of data on different cross-sectional units over a given
period of time while a pooled data set consists of data on the same cross-sectional
units over a given period of time.
b. A panel data set consists of data on the same cross-sectional units over a
given
period of time while a pooled data set consists of data on different cross-
sectional units over a given period of time
c. A panel data consists of data on a single variable measured at a given point in
time while a pooled data set consists of data on the same cross-sectional units over
a given period of time.
d. A panel data set consists of data on a single variable measured at a given point in
time while a pooled data set consists of data
on more than one variable at a given
point in time.
14. _____ has a causal effect on _____.

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a. Income; unemployment
b. Height; health
c. Income; consumption
d. Age; wage
15. Which of the following is true?
a. A variable has a causal effect on another variable if both variables increase or
decrease simultaneously.
b. The notion of ‘ceteris paribus’ plays an important role in causal analysis.
c. Difficulty in inferring causality disappears when studying data at fairly high levels
of aggregation.
d. The problem of inferring causality arises if experimental data is used for analysis.
16. Experimental data are sometimes called retrospective data.
Answer: False
17. An economic model consists of mathematical equations that describe
various relationships between economic variables.
Answer: True
18. A cross-sectional data set consists of observations on a variable or several
variables over time.
Answer: False
19. A time series data is also called a longitudinal data set.
Answer: False
20. The notion of ceteris paribus means “other factors being equal.”
Answer: True
21. Experimental data are easy to obtain in the social sciences.
Answer: False
22. Random sampling complicates the analysis of cross-sectional data.
Answer: False
23. Which of the following terms measures the association between two
variables?
a. Correlation
b. Casual effect
c. Average
d. Independent
24. The constant of economic models are referred to as…
a. error terms
b. parameters
c. hypotheses
d. statistics
25. Which of the following is true of time series data?
Answer: The chronological ordering of observations in a time series conveys
potentially important.

Chapter 2

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1. A dependent variable is also known as a(n) _____.

a. explanatory variable
b. control variable
c. predictor variable
d. response variable
2. If a change in variable x causes a change in variable y, variable x is called
the _____.
a. dependent variable
b. explained variable
c. explanatory variable
d. response variable
3. In the equation y = β0 + β1x + u, β0 is the _____.
a. dependent variable
b. independent variable
c. slope parameter
d. intercept parameter
4. In the equation y = β0 +β1x + u, what is the estimated value of β0?

5. In the equation c = β0 + β1i + u, c denotes consumption and i denotes

income. What is the residual for the 5 observation if c5=$500 and ^c5=$475?
th

a. $975
b. $300
c. $25
d. $50
6. What does the equation ^y=^β0+^β1x denote if the regression equation is y
= β0+β1x1 + u?
a. The explained sum of squares
b. The total sum of squares

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c. The sample regression function

d. The population regression function
7. Consider the following regression model: y = β0 + β1x1 + u. Which of the
following is a property of Ordinary Least Square (OLS) estimates of this model
and their associated statistics?
a. The sum, and therefore the sample average of the OLS residuals, is positive.
b. The sum of the OLS residuals is negative.
c. The sample covariance between the regressors and the OLS residuals is positive.
d. The point ( ́ x, ́ y) always lies on the OLS regression line

8. The explained sum of squares for the regression function, yi=β0+β1x1+u1, is

defined as _____.
a.∑i=1n(yi− ́ y)2
b.∑i=1n(yi−^y)2
c.∑i=1n^ui
d.∑i=1n(ui)2
9. If the total sum of squares (SST) in a regression equation is 81, and the
residual sum of squares (SSR) is 25, what is the explained sum of squares
(SSE)?
a. 64
b. 56
c. 32
d. 18
10. If the residual sum of squares (SSR) in a regression analysis is 66 and the
total sum of squares (SST) is equal to 90, what is the value of the coefficient of
determination?
a. 0.73
b. 0.55
c. 0.27
d. 1.2
11. Which of the following is a nonlinear regression model?
a. y = β0 + β1x1/2 + u
b. log y = β0 + β1log x +u
c. y = 1 / (β0 + β1x) + u
d. y = β0 + β1x + u
12. Which of the following is assumed for establishing the unbiasedness of
Ordinary Least Square (OLS) estimates?
a. The error term has an expected value of 1 given any value of the explanatory
variable.
b. The regression equation is linear in the explained and explanatory variables.
c. The sample outcomes on the explanatory variable are all the same value.
d. The error term has the same variance given any value of the explanatory

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variable.
13. The error term in a regression equation is said to exhibit homoskedasticty
if _____.
a. it has zero conditional mean
b. it has the same variance for all values of the explanatory variable.
c. it has the same value for all values of the explanatory variable
d. if the error term has a value of one given any value of the explanatory variable.
14. In the regression of y on x, the error term exhibits heteroskedasticity if
_____.
a. it has a constant variance
b. Var(y|x) is a function of x
c. x is a function of y
d. y is a function of x
15. What is the estimated value of the slope parameter when the regression
equation, y = β0 + β1x1 + u passes through the origin?

16. A natural measure of the association between two random variables is the
correlation coefficient.
Answer: True
17. The sample covariance between the regressors and the Ordinary Least
Square (OLS) residuals is always positive.
Answer: False
18. R is the ratio of the explained variation compared to the total variation.
2

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Answer: True
19. There are n-1 degrees of freedom in Ordinary Least Square residuals.
Answer: False
20. The variance of the slope estimator increases as the error variance
decreases.
Answer: False
21.7. If xi and yi are positively correlated in the sample then the estimated
slope is….
a. less than zero
b. greater than zero
c. equal to zero
d. equal to one
22.8 The sample correlation between xi and yi is denoted by…
a. B^1
b.
c.
d. p^xy
23.14. In a regression equation, changing the units of measurement of only the
independent variable does not effect the…
a. dependent variable
b. slope
c. intercept
d. error term
24.20. Simple regression is an analysis of correlation between two variables.
Answer: True
25.25. In general, the constant that produces the smallest sum of squared
deviations is always the sample average.
Answer: True

Chapter 3
1. In the equation, y=β0+β1x1+β2x2+u, β2 is a(n) _____.
a. independent variable
b. dependent variable
c. slope parameter
d. intercept parameter
2. Consider the following regression equation: y=β1+β2x1+β2x2+u. What does
β1 imply?
a. β1 measures the ceteris paribus effect of x1 on x2.
b. β1 measures the ceteris paribus effect of y on x1.
c. β1 measures the ceteris paribus effect of x1 on y.
d. β1 measures the ceteris paribus effect of x1 on u.
3. If the explained sum of squares is 35 and the total sum of squares is 49,
what is the residual sum of squares?

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a. 10
b. 12
c. 18
d. 14
4. Which of the following is true of R ? 2

a. R2 is also called the standard error of regression.

b. A low R2 indicates that the Ordinary Least Squares line fits the data well.
c. R2 usually decreases with an increase in the number of independent variables in a
regression.
d. R2 shows what percentage of the total variation in the dependent variable, Y,
is
explained by the explanatory variables.
5. The value of R2 always _____.
a. lies below 0
b. lies above 1
c. lies between 0 and 1
d. lies between 1 and 1.5
6. If an independent variable in a multiple linear regression model is an exact
linear combination of other independent variables, the model suffers from the
problem of _____.
a. perfect collinearity
b. homoskedasticity
c. heteroskedasticty
d. omitted variable bias
7. The assumption that there are no exact linear relationships among the
independent variables in a multiple linear regression model fails if _____,
where n is the sample size and k is the number of parameters.
a. n>2
b. n=k+1
c. n>k
d. n<k+1
8. Exclusion of a relevant variable from a multiple linear regression model
leads to the problem of _____.
a. misspecification of the model
b. multicollinearity
c. perfect collinearity
d. homoskedasticity
9. Suppose the variable x2 has been omitted from the following regression
equation, y=β0+β1x1+β2x2+u. ~β1 is the estimator obtained when x2 is omitted
from the equation. The bias in ~β1 is positive if _____.
a.β2 >0 and x 1 and x 2 are positively correlated
b.β2 <0 and x 1 and x 2 are positively correlated
c.β2 >0 and x 1 and x 2 are negatively correlated
d.β2 = 0 and x 1 and x 2 are negatively correlated

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10. Suppose the variable x2 has been omitted from the following regression
equation, y=β0+β1x1+β2x2+u. ~β1 is the estimator obtained when x2 is omitted
from the equation. The bias in ~β1 is negative if _____.
a. β2 >0 and x 1 and x 2 are positively correlated
b. β2 <0 and x 1 and x 2 are positively correlated
c. β2 =0 and x 1 and x 2 are negatively correlated
d. β2 =0 and x 1 and x 2 are negatively correlated
11. Suppose the variable x2 has been omitted from the following regression
equation, y=β0+β1x1+β2x2+u. ~β1 is the estimator obtained when x2 is omitted
from the equation. If E(~β1) >β1, ~β1 is said to _____.fv
a. have an upward bias
b. have a downward bias
c. be unbiased
d. be biased toward zero
12. High (but not perfect) correlation between two or more independent
variables is called _____.
a. heteroskedasticty
b. homoskedasticty
c. multicollinearity
d. micronumerosity
13. The term _____ refers to the problem of small sample size.
a. micronumerosity
b. multicollinearity
c. homoskedasticity
d. heteroskedasticity
14. Find the degrees of freedom in a regression model that has 10
observations and 7 independent variables.
a. 17
b. 2
c. 3
d. 4
15. The Gauss-Markov theorem will not hold if _____.
a. the error term has the same variance given any values of the explanatory
variables
b. the error term has an expected value of zero given any values of the
independent
variables
c. the independent variables have exact linear relationships among them
d. the regression model relies on the method of random sampling for
collection of
data
16. The term “linear” in a multiple linear regression model means that the
equation is linear in parameters.
Answer: True

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17. The key assumption for the general multiple regression model is that all
factors in the unobserved error term be correlated with the explanatory
variables.
Answer: False
18. The coefficient of determination (R2) decreases when an independent
variable is added to a multiple regression model.
Answer: False
19. An explanatory variable is said to be exogenous if it is correlated with the
error term.
Answer: False
20. A larger error variance makes it difficult to estimate the partial effect of any
of the independent variables on the dependent variable.
Answer: True
21.3. In econometrics, the general partialling out result is usually called the…
a. Gauss-Markov assumption
b. Best linear unbiased estimator
c. Frisch-Waugh theorem
d. Gauss-Markov theorem

22. 15 ignores the error variance increase because it treats

both regressors as…
a. random
b. nonrandom
c. independent
d. dependent
23.22. If two regressions use different sets of observations, then we can tell
how the R-squareds will compare, even if one regression uses a subset of
regressors.
Answer: False
24.25. When one randomly samples from a population, the total sample
variation in xj decreases without bound as the sample size increases.
Answer: False
25. BLUE

Chapter 4
1. The normality assumption implies that:
a. the population error u is dependent on the explanatory variables and is normally
distributed with mean equal to one and variance σ2.
b. the population error u is independent of the explanatory variables and is normally
distributed with mean equal to one and variance σ.
c. the population error u is dependent on the explanatory variables and is normally
distributed with mean zero and variance σ.

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d. the population error u is independent of the explanatory variables and is

normally
distributed with mean zero and variance σ2.
2. Which of the following statements is true?
a. Taking a log of a nonnormal distribution yields a distribution that is closer
to
normal.
b. The mean of a nonnormal distribution is 0 and the variance is σ 2.
c. The CLT assumes that the dependent variable is unaffected by unobserved
factors.
d. OLS estimators have the highest variance among unbiased estimators.
3. A normal variable is standardized by:
a. subtracting off its mean from it and multiplying by its standard deviation.
b. adding its mean to it and multiplying by its standard deviation.
c. subtracting off its mean from it and dividing by its standard deviation.
d. adding its mean to it and dividing by its standard deviation.
4. Which of the following is a statistic that can be used to test hypotheses
about a single population parameter?
a. F statistic
b. t statistic
c. χ2 statistic
d. Durbin Watson statistic
5. Consider the equation, Y = β1 + β2X2 + u. A null hypothesis, H0: β2 = 0 states
that:
a. X2 has no effect on the expected value of β2.
b. X2 has no effect on the expected value of Y.
c. β2 has no effect on the expected value of Y.
d. Y has no effect on the expected value of X2.
6. The significance level of a test is:
a. the probability of rejecting the null hypothesis when it is false.
b. one minus the probability of rejecting the null hypothesis when it is false.
c. the probability of rejecting the null hypothesis when it is true.
d. one minus the probability of rejecting the null hypothesis when it is true.
7. The general t statistic can be written as:

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8. Which of the following statements is true of confidence intervals?

a. Confidence intervals in a CLM are also referred to as point estimates.
b. Confidence intervals in a CLM provide a range of likely values for the
population
parameter.
c. Confidence intervals in a CLM do not depend on the degrees of freedom of a
distribution.
d. Confidence intervals in a CLM can be truly estimated when heteroskedasticity is
present.
9. Which of the following statements is true?
a. When the standard error of an estimate increases, the confidence interval for the
estimate narrows down.
b. Standard error of an estimate does not affect the confidence interval for the
estimate.
c. The lower bound of the confidence interval for a regression coefficient, say β j, is
given by ^βJ - [standard error × (^βJ)]
d. The upper bound of the confidence interval for a regression coefficient, say
βj, is given by ^βJ + [Critical value × standard error (^βJ)]

10. Which of the following tools is used to test multiple linear restrictions?
a. t test
b. z test
c. F test
d. Unit root test
11. Which of the following statements is true of hypothesis testing?
a. The t test can be used to test multiple linear restrictions.

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b. A test of single restriction is also referred to as a joint hypotheses test.

c. A restricted model will always have fewer parameters than its unrestricted
model.
d. OLS estimates maximize the sum of squared residuals.
12. Which of the following correctly defines F statistic if SSR r represents sum
of squared residuals from the restricted model of hypothesis testing, SSR ur
represents sum of squared residuals of the unrestricted model, and q is the
number of restrictions placed?

13. Which of the following statements is true?

a. If the calculated value of F statistic is higher than the critical value, we reject the
alternative hypothesis in favor of the null hypothesis.
b. The F statistic is always nonnegative as SSR r is never smaller than SSRur.
c. Degrees of freedom of a restricted model is always less than the degrees of
freedom of an unrestricted model.
d. The F statistic is more flexible than the t statistic to test a hypothesis with a single
restriction.
14. If R2ur = 0.6873, R2r = 0.5377, number of restrictions = 3, and n – k – 1 = 229,
F statistic equals:
a. 21.2
b. 28.6
c. 36.5
d. 42.1
15. Which of the following correctly identifies a reason why some authors
prefer to report the standard errors rather than the t statistic?
a. Having standard errors makes it easier to compute confidence intervals.

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b. Standard errors are always positive.

c. The F statistic can be reported just by looking at the standard errors.
d. Standard errors can be used directly to test multiple linear regressions.
16. Whenever the dependent variable takes on just a few values it is close to a
normal distribution.
Answer: False
17. If the calculated value of the t statistic is greater than the critical value, the
null hypothesis, H0 is rejected in favor of the alternative hypothesis, H 1.
Answer: True
18. H1: βj ≠ 0, where βj is a regression coefficient associated with an
explanatory variable, represents a one-sided alternative hypothesis.
Answer: False
19. If^β1 and ^β2 are estimated values of regression coefficients associated
with two explanatory variables in a regression equation, then the standard
error (^β1 –^β2) = standard error (^β1) – standard error (^β2).
Answer: False
20. Standard errors must always be positive.
Answer: True
21. The ordinary least square estimators have the smallest variance among all
the unbiased estimators.
Answer: True
22. Which of the following is true of confidence intervals?
Answer: Confidence intervals are also called interval estimates
23. The population parameter in the null hypothesis…
Answer: is not always equal to zero
24. In regression analysis, the standard errors should not always be included
along with the estimated coefficients.
Answer: False

Chapter 5
1. Which of the following statements is true?
a. The standard error of a regression, ^σ, is not an unbiased estimator for σ, the
standard deviation of the error, u, in a multiple regression model.
b. In time series regressions, OLS estimators are always unbiased.
c. Almost all economists agree that unbiasedness is a minimal requirement for an
estimator in regression analysis.
d. All estimators in a regression model that are consistent are also unbiased.
2. If ^βj, an unbiased estimator of βj, is consistent, then the:
a. distribution of ^βj becomes more and more loosely distributed around βj as the
sample size grows.
b. distribution of ^βj becomes more and more tightly distributed around βj as
the sample size grows.

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c. distribution of ^βj tends toward a standard normal distribution as the sample size
grows.
d. distribution of ^βj remains unaffected as the sample size grows.
3. If ^βj, an unbiased estimator of βj, is also a consistent estimator of βj, then
when the sample size tends to infinity:
a. the distribution of ^βj collapses to a single value of zero.
b. the distribution of ^βj diverges away from a single value of zero.
c. the distribution of ^βj collapses to the single point βj.
d. the distribution of ^βj diverges away from βj.
4. In a multiple regression model, the OLS estimator is consistent if:
a. there is no correlation between the dependent variables and the error term.
b. there is a perfect correlation between the dependent variables and the error
term.
c. the sample size is less than the number of parameters in the model.
d. there is no correlation between the independent variables and the error
term.
5. If the error term is correlated with any of the independent variables, the OLS
estimators are:
a. biased and consistent.
b. unbiased and inconsistent.
c. biased and inconsistent.
d. unbiased and consistent.
6. If δ1 = Cov(x1/x2) / Var(x1) where x1 and x2 are two independent variables in a
regression equation, which of the following statements is true?
a. If x2 has a positive partial effect on the dependent variable, and δ 1 > 0, then
the inconsistency in the simple regression slope estimator associated with x 1
is negative.
b. If x2 has a positive partial effect on the dependent variable, and δ 1 > 0, then
the inconsistency in the simple regression slope estimator associated with x 1
is positive.
c. If x1 has a positive partial effect on the dependent variable, and δ 1 > 0, then
the inconsistency in the simple regression slope estimator associated with x 1
is negative.
d. If x1 has a positive partial effect on the dependent variable, and δ 1 > 0, then
the inconsistency in the simple regression slope estimator associated with x 1
is positive.
7. If OLS estimators satisfy asymptotic normality, it implies that:
a. they are approximately normally distributed in large enough sample sizes.
b. they are approximately normally distributed in samples with less than 10
observations.
c. they have a constant mean equal to zero and variance equal to σ 2.
d. they have a constant mean equal to one and variance equal to σ.

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8. In a regression model, if variance of the dependent variable, y, conditional

on an explanatory variable, x, or Var(y|x), is not constant, _____.
a. the t statistics are invalid and confidence intervals are valid for small sample sizes
b. the t statistics are valid and confidence intervals are invalid for small sample sizes
c. the t statistics confidence intervals are valid no matter how large the sample size
is
d. the t statistics and confidence intervals are both invalid no matter how large
the sample size is
9. If ^βj is an OLS estimator of a regression coefficient associated with one of
the explanatory variables, such that j= 1, 2, ...., n, asymptotic standard error of
^βj will refer to the:
a. estimated variance of ^βj when the error term is normally distributed.
b. estimated variance of a given coefficient when the error term is not normally
distributed.
c. square root of the estimated variance of ^βj when the error term is normally
distributed.
d. square root of the estimated variance of ^βj when the error term is not
normally distributed.
10. A useful rule of thumb is that standard errors are expected to shrink at a
rate that is the inverse of the:
a. square root of the sample size.
b. product of the sample size and the number of parameters in the model.
c. square of the sample size.
d. sum of the sample size and the number of parameters in the model.
11. An auxiliary regression refers to a regression that is used:
a. when the dependent variables are qualitative in nature.
b. when the independent variables are qualitative in nature.
c. to compute a test statistic but whose coefficients are not of direct interest.
d. to compute coefficients which are of direct interest in the analysis.
12. The n-R-squared statistic also refers to the:
a. F statistic.
b. t statistic.
c. z statistic.
d. LM statistic.
13. The LM statistic follows a:
a. t distribution.
b. f distribution.
c. χ2 distribution.
d. binomial distribution.
14. Which of the following statements is true?
a. In large samples there are not many discrepancies between the outcomes of
the
F test and the LM test.
b. Degrees of freedom of the unrestricted model are necessary for using the LM test.

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c. The LM test can be used to test hypotheses with single restrictions only and
provides inefficient results for multiple restrictions.
d. The LM statistic is derived on the basis of the normality assumption.
15. Which of the following statements is true under the Gauss-Markov
assumptions?
a. Among a certain class of estimators, OLS estimators are best linear unbiased, but
are asymptotically inefficient.
b. Among a certain class of estimators, OLS estimators are biased but
asymptotically efficient.
c. Among a certain class of estimators, OLS estimators are best linear
unbiased and asymptotically efficient.
d. The LM test is independent of the Gauss-Markov assumptions.
16. If variance of an independent variable in a regression model, say x 1, is
greater than 0, or Var(x1) > 0, the inconsistency in ^β1 (estimator associated
with x1) is negative, if x1 and the error term are positively related.
Answer: False
17. Even if the error terms in a regression equation, u1, u2,....., un, are not
normally distributed, the estimated coefficients can be normally distributed.
Answer: False
18. A normally distributed random variable is symmetrically distributed about
its mean, it can take on any positive or negative value (but with zero
probability), and more than 95% of the area under the distribution is within two
standard deviations.
Answer: True
19. The F statistic is also referred to as the score statistic.
Answer: False
20. The LM statistic requires estimation of the unrestricted model only.
Answer: False

Chapter 6
1. A change in the unit of measurement of the dependent variable in a model
does not lead to a change in:
a. the standard error of the regression.
b. the sum of squared residuals of the regression.
c. the goodness-of-fit of the regression.
d. the confidence intervals of the regression.
2. Changing the unit of measurement of any independent variable, where log
of the dependent variable appears in the regression:
a. affects only the intercept coefficient.
b. affects only the slope coefficient.
c. affects both the slope and intercept coefficients.
d. affects neither the slope nor the intercept coefficient.
3. A variable is standardized in the sample:

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a. by multiplying by its mean.

b. by subtracting off its mean and multiplying by its standard deviation.
c. by subtracting off its mean and dividing by its standard deviation.
d. by multiplying by its standard deviation.
4. Standardized coefficients are also referred to as:
a. beta coefficients.
b. y coefficients.
c. alpha coefficients.
d. j coefficients.
5. If a regression equation has only one explanatory variable, say x 1, its
standardized coefficient must lie in the range:
a. -2 to 0.
b. -1 to 1.
c. 0 to 1.
d. 0 to 2.
6. In the following equation, gdp refers to gross domestic product, and FDI
refers to foreign direct investment.
log(gdp) = 2.65 + 0.527log(bankcredit) + 0.222FDI
(0.13) (0.022) (0.017)
Which of the following statements is then true?
a. If gdp increases by 1%, bank credit increases by 0.527%, the level of FDI
remaining constant.
b. If bank credit increases by 1%, gdp increases by 0.527%, the level of FDI
remaining constant.
c. If gdp increases by 1%, bank credit increases by log(0.527)%, the level of FDI
remaining constant.
d. If bank credit increases by 1%, gdp increases by log(0.527)%, the level of FDI
remaining constant.
7. In the following equation, gdp refers to gross domestic product, and FDI
refers to foreign direct investment.
log(gdp) = 2.65 + 0.527log(bankcredit) + 0.222FDI
(0.13) (0.022) (0.017)
Which of the following statements is then true?
a. If FDI increases by 1%, gdp increases by approximately 22.2%, the amount of
bank credit remaining constant.
b. If FDI increases by 1%, gdp increases by approximately 26.5%, the amount of
bank credit remaining constant.
c. If FDI increases by 1%, gdp increases by approximately 24.8%, the amount
of
bank credit remaining constant.
d. If FDI increases by 1%, gdp increases by approximately 52.7%, the amount of
bank credit remaining constant.
8. Which of the following statements is true when the dependent variable, y >
0?
a. Taking log of a variable often expands its range.

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b. Models using log(y) as the dependent variable will satisfy CLM assumptions
more
closely than models using the level of y.
c. Taking log of variables make OLS estimates more sensitive to extreme values.
d. Taking logarithmic form of variables make the slope coefficients more responsive
to rescaling.
9. Which of the following correctly identifies a limitation of logarithmic
transformation of variables?
a. Taking log of variables make OLS estimates more sensitive to extreme values in
comparison to variables taken in level.
b. Logarithmic transformations cannot be used if a variable takes on zero or
negative values.
c. Logarithmic transformations of variables are likely to lead to heteroskedasticity.
d. Taking log of a variable often expands its range which can cause inefficient
estimates.
10. Which of the following models is used quite often to capture decreasing or
increasing marginal effects of a variable?
a. Models with logarithmic functions
b. Models with quadratic functions
c. Models with variables in level
d. Models with interaction terms
11. Which of the following correctly represents the equation for adjusted R 2?
a. ́ R2 = 1 – [SSR/(n –1)]/[SST/(n+1)]
b. ́ R2 = 1 – [SSR/(n –k – 1)]/[SST/(n+1)]
c. ́ R2 = 1 – [SSR/(n –k – 1)]/[SST/(n – 1)]
d. ́ R2 = 1 – [SSR]/[SST/(n – 1)]
12. Which of the following correctly identifies an advantage of using adjusted
R2 over R2?
a. Adjusted R2 corrects the bias in R2.
b. Adjusted R2 is easier to calculate than R2.
c. The penalty of adding new independent variables is better understood
through adjusted R2 than R2.
d. The adjusted R2 can be calculated for models having logarithmic functions while
R2 cannot be calculated for such models.
13. Two equations form a nonnested model when:
a. one is logarithmic and the other is quadratic.
b. neither equation is a special case of the other.
c. each equation has the same independent variables.
d. there is only one independent variable in both equations.
14. A predicted value of a dependent variable:
a. represents the difference between the expected value of the dependent variable
and its actual value.
b. is always equal to the actual value of the dependent variable.
c. is independent of explanatory variables and can be estimated on the basis of the

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residual error term only.

d. represents the expected value of the dependent variable given particular
values
for the explanatory variables.
15. Residual analysis refers to the process of:
a. examining individual observations to see whether the actual value of a
dependent variable differs from the predicted value.
b. calculating the squared sum of residuals to draw inferences for the
consistency of
estimates.
c. transforming models with variables in level to logarithmic functions so as to
understand the effect of percentage changes in the independent variable on
the
dependent variable.
d. sampling and collection of data in such a way to minimize the squared sum
of
residuals.
16. Beta coefficients are always greater than standardized coefficients.
Answer: False
17. If a new independent variable is added to a regression equation, the
adjusted R2 increases only if the absolute value of the t statistic of the new
variable is greater than one.
Answer: True
18. F statistic can be used to test nonnested models.
Answer: False
19. Predictions of a dependent variable are subject to sampling variation.
Answer: True
20. To make predictions of logarithmic dependent variables, they first have to
be converted to their level forms.
Answer: False
21.11. One popular measure to describe the relationship between the
dependent variable y and each explanatory variable is the:
Answer: average partial effect.
22.15. An independent variable can be included in a regression model
Answer: when it affects y and is uncorrelated will all of the independent variables of
interest.

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23.18. to estimate a0, then the residual for predicting y i is:

24. 20. The centering of explanatory variables about their sample averages
before creating quadratics or interactions forces the coefficient on the levels
to be average partial effects.
Answer: True
25. 22. If the R-squared value is low, then using OLS equation is very easy to
predict individual future outcomes on y given a set of values for the
explanatory variables.
Answer: False

Chapter 7
1. A _____ variable is used to incorporate qualitative information in a
regression model.
a. dependent
b. continuous
c. binomial
d. dummy
2. In a regression model, which of the following will be described using a
binary variable?
a. Whether it rained on a particular day or it did not
b. The volume of rainfall during a year
c. The percentage of humidity in air on a particular day
d. The concentration of dust particles in air
3. Which of the following is true of dummy variables?
a. A dummy variable always takes a value less than 1.
b. A dummy variable always takes a value higher than 1.
c. A dummy variable takes a value of 0 or 1.
d. A dummy variable takes a value of 1 or 10.

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4. Refer to the model above. The inclusion of another binary variable in this
model that takes a value of 1 if a person is uneducated, will give rise to the
problem of _____.
a. omitted variable bias
b. self-selection
c. dummy variable trap
d. heteroskedastcity
5. Refer to the model above. The benchmark group in this model is _____.
a. the group of educated people
b. the group of uneducated people
c. the group of individuals with a high income
d. the group of individuals with a low income
6. Refer to the above model. If ∂0 > 0, _____.
a. uneducated people have higher savings than those who are educated
b. educated people have higher savings than those who are not educated
c. individuals with lower income have higher savings
d. individual with lower income have higher savings
7. The income of an individual in Budopia depends on his ethnicity and
several other factors which can be measured quantitatively. If there are 5
ethnic groups in Budopia, how many dummy variables should be included in
the regression equation for income determination in Budopia?
a. 1
b. 5
c. 6
d. 4
8. The quarterly increase in an employee’s salary depends on the rating of his
work by his employer and several other factors as shown in the model below:
Increase in salary= β0+∂0Rating + other factors. The variable ‘Rating’ is a(n)
_____ variable.
a. dependent variable
b. ordinal variable
c. continuous variable
d. Poisson variable
9. Which of the following is true of Chow test?
a. It is a type of t test.
b. It is a type of sign test.
c. It is only valid under homoskedasticty.
d. It is only valid under heteroskedasticity.
10. Which of the following is true of dependent variables?
a. A dependent variable can only have a numerical value.
b. A dependent variable cannot have more than 2 values.
c. A dependent variable can be binary.
d. A dependent variable cannot have a qualitative meaning.
11. In the following regression equation, y is a binary variable:
y= β0+β1x1+...βk xk+ u

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In this case, the estimated slope coefficient, ^β1 measures _____.

a. the predicted change in the value of y when x1 increases by one unit,
everything else remaining constant
b. the predicted change in the value of y when x1 decreases by one unit,
everything else remaining constant
c. the predicted change in the probability of success when x 1 decreases by
one unit, everything else remaining constant
d. the predicted change in the probability of success when x1 increases by one
unit, everything else remaining constant
12. Consider the following regression equation: y = β0+β1x1+...βk xk+ u
In which of the following cases, the dependent variable is binary?
a. y indicates the gross domestic product of a country
b. y indicates whether an adult is a college dropout
c. y indicates household consumption expenditure
d. y indicates the number of children in a family
Answer: b
13. Which of the following Gauss-Markov assumptions is violated by the linear
probability model?
a. The assumption of constant variance of the error term.
b. The assumption of zero conditional mean of the error term.
c. The assumption of no exact linear relationship among independent
variables.
d. The assumption that none of the independent variables are constants.
14. Which of the following problems can arise in policy analysis and program
evaluation using a multiple linear regression model?
a. There exists homoscedasticity in the model.
b. The model can produce predicted probabilities that are less than zero and
greater
than one.
c. The model leads to the omitted variable bias as only two independent
factors can
be included in the model.
d. The model leads to an overestimation of the effect of independent variables
on
the dependent variable.
15. Consider the following regression equation: y = β0+β1x1+...βk xk+ u
In which of the following cases, is ‘y’ a discrete variable?
a. y indicates the gross domestic product of a country
b. y indicates the total volume of rainfall during a year
c. y indicates household consumption expenditure
d. y indicates the number of children in a family
16. A binary variable is a variable whose value changes with a change in the
number of observations.
Answer: False

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17. A dummy variable trap arises when a single dummy variable describes a
given number of groups.
Answer: False
18. The dummy variable coefficient for a particular group represents the
estimated difference in intercepts between that group and the base group.
Answer: True
19. The multiple linear regression model with a binary dependent variable is
called the linear probability model.
Answer: True
20. A problem that often arises in policy and program evaluation is that
individuals (or firms or cities) choose whether or not to participate in certain
behaviors or programs.
Answer: True
21. 8. The sum of squared residuals form of the F statistic can be computed
easily even when many independent variables are involved; this particular F
statistic is usually called the _____ in econometrics.
a. Chow statistic
b. t statistic
c. statistic
d. LM statistic
22. 16. In a self-selection problem, the explanatory variables can be:
a. endogenous.
b. exogenous.
c. independent.
d.random.
23. 17. A binary response is the most extreme form of a discrete random
variable that takes on:
a. only two values, zero and one.
b. only one value, zero.
c. only one value, one.
d. any value.

20.Ifthep-
valueofanFstati
stic2.63is0.034,
thenwecansayt

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hattheproblemof
interestissignifc
antatthe
5%level.

a.True

b.Fals
e
24. 20. If the p-value of an F statistic 2.63 is 0.034, then we can say that the
problem of interest is significant at the 5% level.
a. True
b. False
25. 25. The parameters in a linear probability model can be interpreted as
measuring the change in the probability that y = 1 due to a one-unit increase in
an explanatory variable.
a. True
b. False

Chapter 8
1. Which of the following is true of heteroskedasticity?
a. Heteroskedasticty causes inconsistency in the Ordinary Least Squares
estimators.
b. Population R2 is affected by the presence of heteroskedasticty.
c. The Ordinary Least Square estimators are not the best linear unbiased
estimators if heteroskedasticity is present.

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d. It is not possible to obtain F statistics that are robust to heteroskedasticity

of an unknown form.
2. Consider the following regression model: yi=β0+β1 xi+ui. If the first four
Gauss-Markov assumptions hold true, and the error term contains
heteroskedasticity, then
_____.
a. Var(ui|xi) =0
b. Var(ui|xi) =1
c. Var(ui|xi) = σi2
d. Var(ui|xi) =σ
3. The general form of the t statistic is _____.

4. Which of the following is true of the OLS t statistics?

a. The heteroskedasticity-robust t statistics are justified only if the sample size
is large.
b. The heteroskedasticty-robust t statistics are justified only if the sample size
is small.
c. The usual t statistics do not have exact t distributions if the sample size is
large.
d. In the presence of homoscedasticity, the usual t statistics do not have exact
t distributions if the sample size is small.
5. The heteroskedasticity-robust _____ is also called the heteroskedastcity-
robust Wald statistic.
a. t statistic
b. F statistic
c. LM statistic
d. z statistic

6. The square root of the quantity is called the … for Bj.

a. heteroskedasticity-robust t statistic.
b. heteroskedasticity-robust standard error
c. heteroskedasticity-robust F statistic
d. heteroskedasticity-robust Wald statistic

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76. Which of the following tests helps in the detection of heteroskedasticity?

a. The Breusch-Pagan test
b. The Breusch-Godfrey test
c. The Durbin-Watson test
d. The Chow test
87. What will you conclude about a regression model if the Breusch-Pagan
test results in a small p-value?
a. The model contains homoskedasticty.
b. The model contains heteroskedasticty.
c. The model contains dummy variables.
d. The model omits some important explanatory factors.
98. A test for heteroskedasticty can be significant if _____.
a. the Breusch-Pagan test results in a large p-value
b. the White test results in a large p-value
c. the functional form of the regression model is misspecified
d. the regression model includes too many independent variables
109. Which of the following is a difference between the White test and the
Breusch-Pagan test?
a. The White test is used for detecting heteroskedasticty in a linear regression
model while the Breusch-Pagan test is used for detecting autocorrelation.
b. The White test is used for detecting autocorrelation in a linear regression
model while the Breusch-Pagan test is used for detecting heteroskedasticity. .
c. The number of regressors used in the White test is larger than the number
of regressors used in the Breusch-Pagan test.
d. The number of regressors used in the Breusch-Pagan test is larger than the
number of regressors used in the White test.
110. Which of the following is true of the White test?
a. The White test is used to detect the presence of multicollinearity in a linear
regression model.
b. The White test cannot detect forms of heteroskedasticity that invalidate the
usual
Ordinary Least Squares standard errors.
c. The White test can detect the presence of heteroskedasticty in a linear
regression model even if the functional form is misspecified.
d. The White test assumes that the square of the error term in a regression
model is uncorrelated with all the independent variables, their squares and
cross products.
121. Which of the following is true?
a. In ordinary least squares estimation, each observation is given a different
weight.
b. In weighted least squares estimation, each observation is given an identical
weight.
c. In weighted least squares estimation, less weight is given to observations
with a higher error variance.

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d. In ordinary least squares estimation, less weight is given to observations

with a lower error variance.
132. Weighted least squares estimation is used only when _____.
a. the dependent variable in a regression model is binary
b. the independent variables in a regression model are correlated
c. the error term in a regression model has a constant variance
d. the functional form of the error variances is known
143. Consider the following regression equation: y=β0+β1x1+u.
Which of the following indicates a functional form misspecification in E(y|x)?
a. Ordinary Least Squares estimates equal Weighted Least Squares estimates.
b. Ordinary Least Squares estimates exceed Weighted Least Squares
estimates by a small magnitude.
c. Weighted Least Squares estimates exceed Ordinary Least Squares
estimates by a small magnitude.
d. Ordinary Least Square estimates are positive while Weighted Least Squares
estimates are negative.
154. Which of the following tests is used to compare the Ordinary Least
Squares (OLS) estimates and the Weighted Least Squares (WLS) estimates?
a. The White test
b. The Hausman test
c. The Durbin-Watson test
d. The Breusch-Godfrey test
16. The generalized least square (GLS) is an efficient procedure that weights
each squared residual by the:
a. conditional variance of ui given xi
b. expected value of ui given xi
c. inverse of the conditional variance of ui given xi
d. square root of the inverse of the conditional variance of ui given xi

175. The linear probability model contains heteroskedasticity unless _____.

a. the intercept parameter is zero
b. all the slope parameters are positive
c. all the slope parameters are zero
d. the independent variables are binary
18. Which of the following is true?
a. If we can estimate hi for each i, it means that we can proceed directly with WLS
estimation.
b. The WLS method fails if hi is negative or zero for any observation
c. The simplest way to deal with homoskedasticity in the linear probability model is
to continue to use OLS estimation.
d. The probability p(x) depends on the error term.
196. The interpretation of goodness-of-fit measures changes in the presence
of heteroskedasticity.
Answer: False

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20. The population R-squared is affected when heteroskedasticity is present in

Var(u/x1,…,xk).
Answer: False
2117. Multicollinearity among the independent variables in a linear regression
model causes the heteroskedasticity-robust standard errors to be large.
Answer: True
22. When the error variance differs across the two groups, we can obtain a
heteroskedasticity-robust Chow test by including a dummy variable
distinguishing the two groups along with interactions between that dummy
variable and all other explanatory variables.
Answer: True
2318. If the Breusch-Pagan Test for heteroskedasticity results in a large p-
value, the null hypothesis of homoskedasticty is rejected.
Answer: False
2419. The generalized least square estimators for correcting
heteroskedasticity are called weighed least squares estimators.
Answer: True
250. The linear probability model always contains heteroskedasticity when the
dependent variable is a binary variable unless all of the slope parameters are
zero.
Answer: True

Chapter 9
1. Consider the following regression model: log(y) = β 0 + β1x1 + β2x12 + β3x3 + u.
This model will suffer from functional form misspecification if _____.
a. β0 is omitted from the model
b. u is heteroskedastic
c. x12 is omitted from the model
d. x3 is a binary variable
2. A regression model suffers from functional form misspecification if _____.
a. a key variable is binary.
b. the dependent variable is binary.
c. an interaction term is omitted.
d. the coefficient of a key variable is zero.
3. Which of the following is true?
a. A functional form misspecification can occur if the level of a variable is used
when the logarithm is more appropriate.
b. A functional form misspecification occurs only if a key variable is
uncorrelated with the error term. .
c. A functional form misspecification does not lead to biasedness in the
ordinary least squares estimators.

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d. A functional form misspecification does not lead to inconsistency in the

ordinary least squares estimators.
4. Which of the following is true of Regression Specification Error Test
(RESET)?
a. It tests if the functional form of a regression model is misspecified.
b. It detects the presence of dummy variables in a regression model.
c. It helps in the detection of heteroskedasticity when the functional form of
the model is correctly specified.
d. It helps in the detection of multicollinearity among the independent
variables in a regression model.
5. A proxy variable _____.
a. increases the error variance of a regression model
b. cannot contain binary information
c. is used when data on a key independent variable is unavailable
d. is detected by running the Davidson-MacKinnon test
6. Which of the following assumptions is needed for the plug-in solution to the
omitted variables problem to provide consistent estimators?
a. The error term in the regression model exhibits heteroskedasticity.
b. The error term in the regression model is uncorrelated with all the
independent variables.
c. The proxy variable is uncorrelated with the dependent variable.
d. The proxy variable has zero conditional mean.
7. Which of the following is a drawback of including proxy variables in a
regression model?
a. It leads to misspecification analysis.
b. It reduces the error variance.
c. It increases the error variance.
d. It exacerbates multicollinearity.
8. Consider the following equation for household consumption expenditure:
Consmptn= β0+ β1Inc + β2Consmptn-1+ u where ‘Consmptn’ measures the
monthly consumption expenditure of a household, ‘Inc’ measures household
income and ‘Consmptn-1’ is the consumption expenditure in the previous
month. Consmptn-1 is a ____ variable.
a. exogenous
b. binary variable
c. lagged dependent
d. proxy variable
9. A measurement error occurs in a regression model when _____.
a. the observed value of a variable used in the model differs from its actual
value
b. the dependent variable is binary
c. the partial effect of an independent variable depends on unobserved factors
d. the model includes more than two independent variables
10. The classical errors-in-variables (CEV) assumption is that _____.

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a. the error term in a regression model is correlated with all observed

explanatory variables
b. the error term in a regression model is uncorrelated with all observed
explanatory variables
c. the measurement error is correlated with the unobserved explanatory
variable
d. the measurement error is uncorrelated with the unobserved explanatory
variable
11. Which of the following is true of measurement error?
a. If measurement error in a dependent variable has zero mean, the ordinary
least squares estimators for the intercept are biased and inconsistent.
b. If measurement error in an independent variable is uncorrelated with the
variable, the ordinary least squares estimators are unbiased.
c. If measurement error in an independent variable is uncorrelated with other
independent variables, all estimators are biased.
d. If measurement error in a dependent variable is correlated with the
independent variables, the ordinary least squares estimators are unbiased.
12. Sample selection based on the dependent variable is called _____.
a. random sample selection
b. endogenous sample selection
c. exogenous sample selection
d. stratified sample selection
13. The method of data collection in which the population is divided into
nonoverlapping, exhaustive groups is called _____.
a. random sampling
b. stratified sampling
c. endogenous sampling
d. exogenous sampling
14. Which of the following types of sampling always causes bias or
inconsistency in the ordinary least squares estimators?
a. Random sampling
b. Exogenous sampling
c. Endogenous sampling
d. Stratified sampling
15. Which of the following is a difference between least absolute deviations
(LAD) and ordinary least squares (OLS) estimation?
a. OLS is more computationally intensive than LAD.
b. OLS is more sensitive to outlying observations than LAD.
c. OLS is justified for very large sample sizes while LAD is justified for smaller
sample sizes.
d. OLS is designed to estimate the conditional median of the dependent
variable while LAD is designed to estimate the conditional mean.
16. An explanatory variable is called exogenous if it is correlated with the error
term.
Answer: False

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17. A multiple regression model suffers from functional form misspecification

when it does not properly account for the relationship between the dependent
and the observed explanatory variables.
Answer: True
18. The measurement error is the difference between the actual value of a
variable and its reported value.
Answer: True
19. Studentized residuals are obtained from the original OLS residuals by
dividing them by an estimate of their standard deviation.
Answer: True
20. The Least Absolute Deviations (LAD) estimators in a linear model minimize
the sum of squared residuals.
Answer: False
21. 13. A complete cases estimator is an estimator that uses:
a. complete information about the residuals.
b. only observations with a complete set of data on y and x1, ..., xk.
c. complete information about the outliers.
d. only observations with a complete set of data on x1, ..., xk.
22. 14. How many new variables should be created for a multiple regression
model where data are always available for y and x1, x2, ..., xk−1 but are
sometimes missing for the explanatory variable xk?
a. Four variables
b. One variable
c. Two variables
Rationale:
FEEDBACK: Two new variables should be created for a multiple regression model
where data are always available for y and x1, x2, ..., xk−1 but are sometimes
missing for the explanatory variable xk. For a unit i, the first variable, say zik, is
defined to be xik when xik is observed, and zero otherwise. The second variable is a
"missing data indicator," say mik,which equals one when xik is missing and equals
zero when xik is observed.
d. Three variables
23. 5. Which of the following is a test of nontested models?
a. Davidson-MacKinnon test
b. Standard F test
c. Regression Specification Error Test
d. White test24. If the data are missing at random, then the missing data do not
cause any statistical problems.
Asnwer: True
24. 5. 24. If the data are missing at random, then the missing data do not cause
any statistical problems.
Asnwer: True

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25. 21. One of the assumptions for the plug-in solution to provide consistent
estimator of β1 and β2 is that the error u is uncorrelated with all the
independent variables.
Answer: True

Chapter 10
1. Which of the following correctly identifies a difference between cross-
sectional data and time series data?
a. Cross-sectional data is based on temporal ordering, whereas time series
data is not.
b. Time series data is based on temporal ordering, whereas cross-sectional
data is not.
c. Cross-sectional data consists of only qualitative variables, whereas time
series data consists of only quantitative variables.
d. Time series data consists of only qualitative variables, whereas cross-
sectional data does not include qualitative variables.
2. A stochastic process refers to a:
a. sequence of random variables indexed by time.
b. sequence of variables that can take fixed qualitative values.
c. sequence of random variables that can take binary values only.
d. sequence of random variables estimated at the same point of time.
3. The sample size for a time series data set is the number of:
a. variables being measured.
b. time periods over which we observe the variables of interest less the
number of variables being measured.
c. time periods over which we observe the variables of interest plus the
number of variables being measured.
d. time periods over which we observe the variables of interest.
4. The model: Yt = β0+β1ct+ ut, t = 1,2,.......n, is an example of a(n):
a. autoregressive conditional heteroskedasticity model.
b. static model.
c. finite distributed lag model.
d. infinite distributed lag model.
5. A static model is postulated when:
a. a change in the independent variable at time ‘t’ is believed to have an effect
on the dependent variable at period ‘t + 1’.
b. a change in the independent variable at time ‘t’ is believed to have an effect
on the dependent variable for all successive time periods.
c. a change in the independent variable at time ‘t’ does not have any effect on
the dependent variable.

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d. a change in the independent variable at time ‘t’ is believed to have an

immediate effect on the dependent variable.
6. Refer to the following model.
yt = α0 + β0st + β1st-1 + β2st-2 + β3st-3 + ut
This is an example of a(n):
a. infinite distributed lag model.
b. finite distributed lag model of order 1.
c. finite distributed lag model of order 2.
d. finite distributed lag model of order 3.
7. Refer to the following model.
yt = α0 + β0st + β1st-1 + β2st-2 + β3st-3 + utβ0 + β1 + β2 + β3 represents:
a. the short-run change in y given a temporary increase in s.
b. the short-run change in y given a perermanent increase in s.
c. the long-run change in y given a permanent increase in s.
d. the long-run change in y given a temporary increase in s.
8. Which of the following is an assumption on which time series regression is
based?
a. A time series process follows a model that is nonlinear in parameters.
b. In a time series process, no independent variable is a perfect linear
combination of the others.
c. In a time series process, at least one independent variable is a constant.
d. For each time period, the expected value of the error u t, given the
explanatory variables for all time periods, is positive.
9. Under the assumptions of time series regression, which of the following
statements will be true of the following model: yt = α0 + α1dt + ut?
a. d can have a lagged effect on y.
b. ut can be correlated with past and future values of d.
c. Changes in the error term cannot cause future changes in d.
d. Changes in d cannot cause changes in y at the same point of time.
10. If an explanatory variable is strictly exogenous it implies that:
a. changes in the lag of the variable does not affect future values of the
dependent variable.
b. the variable is correlated with the error term in all future time periods.
c. the variable cannot react to what has happened to the dependent variable in
the past.
d. the conditional mean of the error term given the variable is zero.
11. A study which observes whether a particular occurrence influences some
outcome is referred to as a(n):
a. event study.
b. exponential study.
c. laboratory study.
d. comparative study.
12. With base year 1990, the index of industrial production for the year 1999 is
112. What will be the value of the index in 1999, if the base year is changed to
1982 and the index measured 96 in 1982?

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a. 112.24
b. 116.66
c. 85.71
d. 92.09
13. Which of the following statements is true?
a. The average of an exponential time series is a linear function of time.
b. The average of a linear sequence is an exponential function of time.
c. When a series has the same average growth rate from period to period, it
can be approximated with an exponential trend.
d. When a series has the same average growth rate from period to period, it
can be approximated with a linear trend.
14. Adding a time trend can make an explanatory variable more significant if:
a. the dependent and independent variables have similar kinds of trends, but
movement in the independent variable about its trend line causes movement
in the dependent variable away from its trend line.
b. the dependent and independent variables have similar kinds of trends and
movement in the independent variable about its trend line causes movement
in the dependent variable towards its trend line.
c. the dependent and independent variables have different kinds of trends and
movement in the independent variable about its trend line causes movement
in the dependent variable towards its trend line.
d. the dependent and independent variables have different kinds of trends, but
movement in the independent variable about its trend line causes movement
in the dependent variable away from its trend line.
15. A seasonally adjusted series is one which:
a. has had seasonal factors added to it.
b. has seasonal factors removed from it.
c. has qualitative explanatory variables representing different seasons.
b. has qualitative dependent variables representing different seasons.
16. Economic time series are outcomes of random variables.
Answer: True
17. In a static model, one or more explanatory variables affect the dependent
variable with a lag.
Answer: False
18. Time series regression Time series regression is based on series which
exhibit serial correlation.
Answer: False
19. Price indexes are necessary for turning a time series measured in real
value into nominal value.
Answer: False
20. Dummy variables can be used to address the problem of seasonality in
regression models.
Answer: True
21. 14. The propensity δ0 + δ1+ … + δk is sometimes called the:

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a. long-run elasticity, which measures the percentage increase in a dependent

variable after k quarters given a permanent 1% increase in the k independent
variables.
b. long-run elasticity, which measures the percentage decrease in a dependent
variable after k quarters given a permanent 1% decrease in the k independent
variables.
c. short-run elasticity, which measures the percentage increase in a dependent
variable after k quarters given a permanent 1% increase in the k independent
variables.
d. short-run elasticity, which measures the percentage decrease in a dependent
variable after k quarters given a permanent 1% decrease in the k independent
variables.
22. 18. If a1>0, then yt in the linear function of time E(yt)=a0+a1t displays a(n):
a. upward trend
b. downward trend
c. exponential trend
d. quadratic trend
23. 23. The short-run elasticity measures the immediate percentage change in
a dependent variable given a 1% increase in the independent variables.
Answer: True
24. 25. When a series has the same average growth rate from period to period,
then it can be approximated by an exponential trend
Answer: True
25. 10. Which of the following rules out perfect collinearity among the
regressors?
a. Multiple regression
b. Simple regression
c. Time series regression
d. Cross-sectional regression

Chapter 11
1. A process is stationary if:
a. any collection of random variables in a sequence is taken and shifted ahead by
h time periods; the joint probability distribution changes.
b. any collection of random variables in a sequence is taken and shifted ahead by
h time periods, the joint probability distribution remains unchanged.
c. there is serial correlation between the error terms of successive time periods and the
explanatory variables and the error terms have positive covariance.
d. there is no serial correlation between the error terms of successive time periods and
the explanatory variables and the error terms have positive covariance.
2. Covariance stationary sequences where Corr(xt+xt+h) → 0 as h → ∞ are said to
be:
a. unit root processes
b. trend-stationary processes

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c. serially uncorrelated
d. asymptotically uncorrelated
32. A stochastic process {xt: t = 1,2,....} with a finite second moment [E(xt2) < ∞] is
covariance stationary if:
a. E(xt) is variable, Var(xt) is variable, and for any t, h ≥ 1, Cov(xt, xt+h) depends
only on ‘h’ and not on ‘t’.
b. E(xt) is variable, Var(xt) is variable, and for any t, h ≥ 1, Cov(xt, xt+h) depends
only on ‘t’ and not on h.
c. E(xt) is constant, Var(xt) is constant, and for any t, h ≥ 1, Cov(xt, xt+h) depends
only on ‘h’ and not on ‘t’.
d. E(xt) is constant, Var(xt) is constant, and for any t, h ≥ 1, Cov(xt, xt+h) depends
only on ‘t’ and not on ‘h’.
43. A covariance stationary time series is weakly dependent if:
a. the correlation between the independent variable at time ‘t’ and the dependent
variable at time ‘t + h’ goes to ∞ as h → 0.
b. the correlation between the independent variable at time ‘t’ and the dependent
variable at time ‘t + h’ goes to 0 as h → ∞.
c. the correlation between the independent variable at time ‘t’ and the
independent variable at time ‘t + h’ goes to ∞ as h → 0.
d. the correlation between the independent variable at time ‘t’ and the
independent variable at time ‘t + h’ goes to 0 as h → ∞.
54. The model yt = et + β1et – 1 + β2et – 2 , t = 1, 2, ..... , where et is an i.i.d. sequence
with zero mean and variance σ2e represents a(n):
a. static model.
b. moving average process of order one.
c. moving average process of order two.
d. autoregressive process of order two.
65. The model xt = α1xt – 1 + et , t =1,2,.... , where et is an i.i.d. sequence with zero
mean and variance σ2e represents a(n):
a. moving average process of order one.
b. moving average process of order two.
c. autoregressive process of order one.
d. autoregressive process of order two.
76. Which of the following is assumed in time series regression?
a. There is no perfect collinearity between the explanatory variables.
b. The explanatory variables are contemporaneously endogenous.
c. The error terms are contemporaneously heteroskedastic.
d. The explanatory variables cannot have temporal ordering.
87. Suppose ut is the error term for time period ‘t’ in a time series regression
model the explanatory variables are xt = (xt1, xt2 ...., xtk). The assumption that the
errors are contemporaneously homoskedastic implies that:
a. Var(ut|xt) = √σ.
b. Var(ut|xt) = ∞.
c. Var(ut|xt) = σ2.
d. Var(ut|xt) = σ.
98. Which of the following statements is true?

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a. A model with a lagged dependent variable cannot satisfy the strict exogeneity
assumption.
b. Stationarity is critical for OLS to have its standard asymptotic properties.
c. Efficient static models can be estimated for nonstationary time series.
d. In an autoregressive model, the dependent variable in the current time period
varies with the error term of previous time periods.
109. Consider the model: yt = α0 + α1rt1 + α2rt2 + ut. Under weak dependence, the
condition sufficient for consistency of OLS is:
a. E(rt1|rt2) = 0.
b. E(yt |rt1, rt2) = 0.
c. E(ut |rt1, rt2) = 0.
d. E(ut |rt1, rt2) = ∞.
110. The model yt = yt – 1 + et, t = 1, 2, ... represents a:
a. AR(2) process.
b. MA(1) process.
c. random walk process.
d. random walk with a drift process.
121. Which of the following statements is true?
a. A random walk process is stationary.
b. The variance of a random walk process increases as a linear function of time.
c. Adding a drift term to a random walk process makes it stationary.
d. The variance of a random walk process with a drift decreases as an
exponential function of time.
132. If a process is said to be integrated of order one, or I(1), _____.
a. it is stationary at level
b. averages of such processes already satisfy the standard limit theorems
c. the first difference of the process is weakly dependent
d. it does not have a unit root
Answer: c
14. Unit root processes, such as a random walk (with or without drift), are said to
be:
a. integrated of order one.
b. integrated of order two.
c. sequentially exogenous.
d. asymptotically uncorrelated.
153. Which of the following statements is true of dynamically complete models?
a. There is scope of adding more lags to the model to better forecast the
dependent variable.
b. The problem of serial correlation does not exist in dynamically complete
models.
c. All econometric models are dynamically complete.
d. Sequential endogeneity is implied by dynamic completeness..
164. In the model yt = α0 + α1xt1 + α2xt2 + ..... + αkxtk + ut, the explanatory variables,
xt = (xt1, xt2 ...., xtk), are sequentially exogenous if:
a. E(ut|xt , xt-1, ......) = E(ut) = 0, t = 1,2, ....
b. E(ut|xt , xt-1, ......) ≠ E(ut) = 0, t = 1,2, ....

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c. E(ut|xt , xt-1, ......) = E(ut) > 0, t = 1,2, ....

d. E(ut|xt , xt-1, ......) = E(ut) = 1, t = 1,2, ....
17. Which of the following is a strong assumption for static and finite distributed
lag models?
a. Dynamic completeness
b. Sequential exogeneity
c. Strict exogeneity
d. Homoskedasticity
185. If ut refers to the error term at time ‘t’ and yt – 1 refers to the dependent
variable at time ‘t – 1’, for an AR(1) process to be homoskedastic, it is required
that:
a. Var(ut|yt – 1) > Var(yt|yt-1)= σ2.
b. Var(ut|yt – 1) = Var(yt|yt-1)> σ2.
c. Var(ut|yt – 1) < Var(yt|yt-1)= σ2.
d. Var(ut|yt – 1) = Var(yt|yt-1)= σ2.
196. Covariance stationarity focusses only on the first two moments of a
stochastic process.
Answer: True
17. Under adaptive expectations, the expected current value of a variable does
not depend on a recently observed value of the variable.
Answer: False
2018. Weakly dependent processes are said to be integrated of order zero.
Answer: True
21. If a process is a covariance stationary process, then it will have a finite
second moment.
Answer: False
2217. Under adaptive expectations, the expected current value of a variable does
not depend on a recently observed value of the variable.
Answer: False
23. The variance of a random walk process decreases as a linear function of time.
Answer: False

2419. Sequential exogeneity is implied by dynamic completeness.

Answer: True
250. The homoskedasticity assumption in time series regression suggests that
the variance of the error term cannot be a function of time.
Answer: True

Chapter 12
1. In the presence of serial correlation:
a. estimated standard errors remain valid.
b. estimated test statistics remain valid.
c. estimated OLS values are not BLUE.
d. estimated variance does not differ from the case of no serial correlation.

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2. When a series is stationary, weakly dependent, and has serial correlation:

a. the adjusted R2 is inconsistent, while R2 is a consistent estimator of the population
parameter.
b. the adjusted R2 is consistent, while R2 is an inconsistent estimator of the
population parameter.
c. both the adjusted R2 and R2 are inconsistent estimators of the population
parameter.
d. both the adjusted R2 and R2 are consistent estimators of the population
parameter.
3. Which of the following is a test for serial correlation in the error terms?
a. Johansen test
b. Dickey Fuller test
c. Durbin Watson test
d. White test
4. For a given significance level, if the calculated value of the Durbin Watson
statistic lies between the lower critical value and the upper critical value,
_____.
a. the hypothesis of no serial correlation is accepted
b. the hypothesis of no serial correlation is rejected
c. the test is inconclusive
d. the hypothesis of heteroskedasticity is accepted
5. Which of the following statements is true?
a. When explanatory variables are not strictly exogenous, the t test for serial
correlation is valid.
b. When explanatory variables are not strictly exogenous, the Durbin Watson test for
serial correlation is valid.
c. Breusch-Godfrey test can be used to check for second order serial correlation.
d. White test can be used to check for second order serial correlation.
6. The Breusch-Godfrey test statistic follows a:
a. χ2 distribution.
b. t distribution.
c. normal distribution.
d. F distribution.
7. In a model based on a weakly dependent time series with serial correlation
and strictly exogenous explanatory variables, _____.
a. the feasible generalized least square estimates are unbiased
b. the feasible generalized least square estimates are BLUE
c. the feasible generalized least square estimates are asymptotically more efficient
than OLS estimates
d. the feasible generalized least square estimates are asymptotically less efficient
than OLS estimates
8. Which of the following is an example of FGLS estimation?
a. Dickey-Fuller estimation
b. Vector error correction estimation
c. Prais-Winsten estimation

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d. OLS estimation.
9. Which of the following is the reason why standard errors measured by OLS
differ from standard errors measured through Prais-Winsten transformation?
a. OLS standard errors account for serial correlation, whereas Prais-Winsten
estimations do not.
b. Prais-Winsten standard errors account for serial correlation, whereas OLS
estimations do not.
c. Prais-Winsten standard errors account for heteroskedasticity, whereas OLS
estimations do not.
d. OLS standard errors account for heteroskedasticity, whereas Prais-Winsten
estimations do not.
10. Which of the following identifies an advantage of first differencing a time-
series?
a. First differencing eliminates most of the serial correlation.
b. First differencing eliminates most of the heteroskedastcicty.
c. First differencing eliminates most of the multicollinearity.
d. First differencing eliminates the possibility of spurious regression.
11. Which of the following is a limitation of serial correlation-robust standard
errors?
a. The serial correlation-robust standard errors are smaller than OLS standard errors
when there is serial correlation.
b. The serial correlation-robust standard errors can be poorly behaved when there is
substantial serial correlation and the sample size is small.
c. The serial correlation-robust standard errors cannot be calculated for
autoregressive processes of an order greater than one.
d. The serial correlation-robust standard errors cannot be calculated after relaxing
the assumption of homoskedasticity.
12. Which of the following statements is true?
a. Prais-Winsten and Cochrane-Orcutt transformations are consistent when
explanatory variables are not strictly exogenous.
b. The SC-robust standard errors cannot be estimated in models with lagged
dependent variables.
c. The SC-robust standard errors work better after quasi-differencing a time series
that is expected to be serially correlated.
d. Estimation of SC-robust standard errors is independent of the sample size.
13. In the presence of heteroskedasticity, the usual OLS estimates of:
a. standard errors are valid, whereas the t statistics and F statistics are invalid.
b. t statistics are valid, but the standard errors and F statistics are invalid.
c. F statistics are valid, but the standard errors and t statistics are invalid.
d. standard errors, t statistics, and F statistics are invalid.
14. Which of the following tests can be used to test for heteroskedasticity in a
time series?
a. Johansen test
b. Dickey-Fuller test
c. Breusch-Pagan test

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d. Durbin’s alternative test

15. The equation u2t = α0 + α1u2t– 1 + vt is an autoregressive model in _____.
a. ut
b. u2t
c. vt
d. ut – 1
16. In presence of serial correlation, the OLS variance formula accurately
estimates the true variance of the OLS estimator.
Answer: False
17. Durbin’s alternative test is valid even if the explanatory variables are
strictly exogenous.
Answer: True
18. Consistency of feasible generalized least square estimators requires the
error term to be correlated with lags of the explanatory variable.
Answer: False
19. FGLS estimates are efficient when explanatory variables are not strictly
exogenous.
Answer: False
20. In time series regressions, it is advisable to check for serial correlation
first, before checking for heteroskedasticity.
Answer: True

Chapter 14
1. Which of the following assumptions is required for obtaining unbiased fixed
effect estimators?
a. The errors are heteroskedastic.
b. The errors are serially correlated.
c. The explanatory variables are strictly exogenous.
d. The unobserved effect is correlated with the explanatory variables.
2. A pooled OLS estimator that is based on the time-demeaned variables is
called the _____.
a. random effects estimator
b. fixed effects estimator
c. least absolute deviations estimator
d. instrumental variable estimator
3. What should be the degrees of freedom (df) for fixed effects estimation if
the data set includes ‘N’ cross sectional units over ‘T’ time periods and the
regression model has ‘k’ independent variables?
a. N-kT
b. NT-k
c. NT-N-k
d. N-T-k

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4. Which of the following types of variables cannot be included in a fixed

effects model?
a. Dummy variable
b. Discrete dependent variable
c. Time-varying independent variable
d. Time-constant independent variable
5. Which of the following is a property of dummy variable regression?
a. This method is best suited for panel data sets with many cross-sectional
observations.
b. The R-squared obtained from this method is lower than that obtained from
regression on time-demeaned data.
c. The degrees of freedom cannot be computed directly with this method.
d. The major statistics obtained from this method are identical to that obtained from
regression on time-demeaned data.
6. Which of the following is a difference between a fixed effects estimator and
a first-difference estimator?
a. The fixed effects estimators are always larger than the first difference estimators
in a two-period panel data analysis.
b. The fixed effects estimator is more efficient than the first-difference estimator
when the idiosyncratic errors are serially uncorrelated.
c. The first difference estimator is more sensitive to nonnormality and
heteroskedasticity.
d. The bias in the first difference estimator depends on the time period (T) of
analysis while the bias in the fixed effect does not depend on T.
7. Which of the following assumptions is required for obtaining unbiased
random effect estimators?
a. The idiosyncratic errors are heteroskedastic.
b. The unobserved effect is independent of all explanatory variables in all time
periods.
c. The idiosyncratic errors are serially correlated.
d. The unobserved effect is correlated with the explanatory variables.
8. The estimator obtained through regression on quasi-demeaned data is
called the _____.
a. random effects estimator
b. fixed effects estimator
c. hetroskedasticity-robust OLS estimator
d. instrumental variables estimator
9. The random effects approach _____.
a. cannot be used if the key explanatory variable is constant over time.
b. is preferred to pooled OLS because RE is generally more efficient.
c. is suitable if the Hausman test rejects the assumption that the unobserved effect
is uncorrelated with the explanatory variables.
d. is more convincing than fixed effects for policy analysis using aggregate data.

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10. The random effects estimate is identical to the fixed effects estimate if the
estimated transformation parameter, ^θ, in generalized least squares
estimation that eliminates serial correlation between error terms is, _____.
a. less than zero
b. equal to zero
c. equal to one
d. greater than one
11. Which of the following is true of the correlated random effects approach
(CRE)?
a. The CRE approach assumes that the unobserved effect is uncorrelated with the
observed explanatory variables.
b. The CRE approach cannot be used if the regression model includes a time-
constant explanatory variable.
c. The CRE approach considers that the unobserved effect is correlated with the
average level of explanatory variables.
d. The CRE estimate equals the random effects estimate.
12. Which of the following is a reason for using the correlated random effects
approach?
a. It provides unbiased and consistent estimators when the idiosyncratic errors are
serially correlated.
b. It provides unbiased and consistent estimators when the idiosyncratic errors are
heteroskedastic.
c. It provides a more efficient estimate than the fixed effects approach.
d. It provides a way to include time-constant explanatory variables in a fixed effects
analysis.
13. In the correlated random effects approach, the regression model includes
_____.
a. time averages as separate explanatory variables
b. at least one dummy variable
c. more than one endogenous explanatory variable
d. an instrumental variable
14. An economist wants to study the effect of income on savings. He collected
data on 120 identical twins. Which of the following methods of estimation is
the most suitable method, if income is correlated with the unobserved family
effect?
a. Random effects estimation
b. Fixed effects estimation
c. Ordinary least squares estimation
d. Weighted Least squares estimation
15. Which of the following statements is true?
a. Fixed effects estimation is not suitable when the unobserved cluster effect is
correlated with one or more explanatory variables.
b. Fixed effects approach is not applicable if the key explanatory variables change
only at the level of the cluster.

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c. The ordinary least squares standard errors are incorrect when there is cluster
effect.
d. Random effects estimation can be applied to a cluster sample only if the
unobserved cluster effect is correlated with one or more explanatory variables.
16. A data set is called an unbalanced panel if it has missing years for at least
some cross-sectional units in the sample.
Answer: True
17. In a random effects model, we assume that the unobserved effect is
correlated with each explanatory variable.
Answer: False
18. The value of the estimated transformation parameter in generalized least
square estimation that eliminates serial correlation in error terms indicates
whether the estimates are likely to be closer to the pooled OLS or the fixed
effects estimates.
Answer: True
19. The correlated random effects approach cannot be applied to models with
many time-varying explanatory variables.
Answer: False
20. Pooled ordinary least squares estimation is commonly applied to cluster
samples when eliminating a cluster effect via fixed effects is infeasible or
undesirable.
Answer: True

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Introductory econometrics test bank 1 1

Consolidated Financial Statement (Trường Đại học Kinh tế Thành phố Hồ Chí Minh)

Studocu is not sponsored or endorsed by any college or university

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 lOMoARcPSD|14898814

Test Bank –Introductory Econometrics: A Modern Approach, 5th Edition by

Jeffrey M. Wooldridge

With PERFECT SOLUTION AVAILABLE OF ALL CHAPTERS

Chapter 1

1. Econometrics is the branch of economics that _____.

a. studies the behavior of individual economic agents in making economic decisions
b. develops and uses statistical methods for estimating economic relationships
c. deals with the performance, structure, behavior, and decision-making of
an economy as a whole
d. applies mathematical methods to represent economic theories and solve
economic problems.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: What is Econometrics?
BUSPROG:
Feedback: Econometrics is the branch of economics that develops and uses
statistical methods for estimating economic relationships.

2. Nonexperimental data is called _____.

a. cross-sectional data
b. time series data
c. observational data
d. panel data

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: What is Econometrics?
BUSPROG:
Feedback:

3. Which of the following is true of experimental data?

a. Experimental data are collected in laboratory environments in the natural
sciences.
b. Experimental data cannot be collected in a controlled environment.
c. Experimental data is sometimes called observational data.
d. Experimental data is sometimes called retrospective data.

Answer: a
Difficulty: Easy

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Bloom’s: Knowledge
A-Head: What is Econometrics?
BUSPROG:
Feedback:

4. An empirical analysis relies on _____to test a theory.

a. common sense
b. ethical considerations
c. data
d. customs and conventions

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Steps in Empirical Economic Analysis
BUSPROG:
Feedback: An empirical analysis relies on data to test a theory.

5. The term ‘u’ in an econometric model is usually referred to as the _____.

a. error term
b. parameter
c. hypothesis
d. dependent variable

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Steps in Empirical Economic Analysis
BUSPROG:
Feedback: The term u in an econometric model is called the error term or
disturbance term.

6. The parameters of an econometric model _____.

a. include all unobserved factors affecting the variable being studied
b. describe the strength of the relationship between the variable under study and
the factors affecting it
c. refer to the explanatory variables included in the model
d. refer to the predictions that can be made using the model

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Steps in Empirical Economic Analysis
BUSPROG:

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Feedback: The parameters of an econometric model describe the direction and

strength of the relationship between the variable under study and the factors
affecting it.

7. Which of the following is the first step in empirical economic analysis?

a. Collection of data
b. Statement of hypotheses
c. Specification of an econometric model
d. Testing of hypotheses

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Steps in Empirical Economic Analysis
BUSPROG:
Feedback: The first step in empirical economic analysis is the specification of the
econometric model.

8. A data set that consists of a sample of individuals, households, firms, cities,

states, countries, or a variety of other units, taken at a given point in time, is called
a(n) _____.
a. cross-sectional data set
b. longitudinal data set
c. time series data set
d. experimental data set

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Structure of Economic Data
BUSPROG:
Feedback: A data set that consists of a sample of individuals, households, firms,
cities, states, countries, or a variety of other units, taken at a given point in time, is
called a cross-sectional data set.

9. Data on the income of law graduates collected at different times during the same
year is_____.
a. panel data
b. experimental data
c. time series data
d. cross-sectional data

Answer: d
Difficulty: Easy

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Bloom’s: Application
A-Head: The Structure of Economic Data
BUSPROG: Analytic
Feedback: A data set that consists of a sample of individuals, households, firms,
cities, states, countries, or a variety of other units, taken at a given point in time, is
called a cross-sectional data set. Therefore, data on the income of law graduates on
a particular year are examples of cross-sectional data.

10. A data set that consists of observations on a variable or several variables over
time is called a _____ data set.
a. binary
b. cross-sectional
c. time series
d. experimental

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Structure of Economic Data
BUSPROG:
Feedback: A time-series data set consists of observations on a variable or several
variables over time.

11. Which of the following is an example of time series data?

a. Data on the unemployment rates in different parts of a country during a year.
b. Data on the consumption of wheat by 200 households during a year.
c. Data on the gross domestic product of a country over a period of 10 years.
d. Data on the number of vacancies in various departments of an organization on a
particular month.

Answer: c
Difficulty: Easy
Bloom’s: Application
A-Head: The Structure of Economic Data
BUSPROG: Analytic
Feedback: A time-series data set consists of observations on a variable or several
variables over
time. Therefore, data on the gross domestic product of a country over a period of 10
years is an example of time series data.

12. Which of the following refers to panel data?

a. Data on the unemployment rate in a country over a 5-year period
b. Data on the birth rate, death rate and population growth rate in developing
countries over a 10-year period.

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c. Data on the income of 5 members of a family on a particular year.
d. Data on the price of a company’s share during a year.

Answer: b
Difficulty: Easy
Bloom’s: Application
A-Head: The Structure of Economic Data
BUSPROG: Analytic

Feedback: A panel data set consists of a time series for each cross-sectional
member in the data set. Therefore, data on the birth rate, death rate and infant
mortality rate in developing countries over a 10-year period refers to panel data.

13. Which of the following is a difference between panel and pooled cross-sectional
data?
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Difficulty: Moderate
Bloom’s: Application
A-Head: Causality and the Notion of Ceteris Paribus in Econometric Analysis
BUSPROG: Analytic
Feedback: Income has a causal effect on consumption because an increase in
income leads to an increase in consumption.

15. Which of the following is true?

a. A variable has a causal effect on another variable if both variables increase or
decrease simultaneously.
b. The notion of ‘ceteris paribus’ plays an important role in causal analysis.
c. Difficulty in inferring causality disappears when studying data at fairly high levels
of aggregation.
d. The problem of inferring causality arises if experimental data is used for analysis.

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Causality and the Notion of Ceteris Paribus in Econometric Analysis
BUSPROG:
Feedback: The notion of ‘ceteris paribus’ plays an important role in causal analysis.

16. Experimental data are sometimes called retrospective data.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: What is Econometrics?
BUSPROG:
Feedback: Nonexperimental data are sometimes called retrospective data.

17. An economic model consists of mathematical equations that describe various

relationships between economic variables.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Steps in Empirical Economic Analysis
BUSPROG:
Feedback: An economic model consists of mathematical equations that describe
various relationships between economic variables.

18. A cross-sectional data set consists of observations on a variable or several

variables over time.

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Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Structure of Economic Data
BUSPROG:
Feedback: A time series data set consists of observations on a variable or several
variables over
time.

19. A time series data is also called a longitudinal data set.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Structure of Economic Data
BUSPROG:
Feedback: A time series data is also called a longitudinal data set.

20. The notion of ceteris paribus means “other factors being equal.”

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Causality and the Notion of Ceteris Paribus in Econometric Analysis
BUSPROG:
Feedback: The notion of ceteris paribus means “other factors being equal.”

Chapter 2

1. A dependent variable is also known as a(n) _____.

a. explanatory variable
b. control variable
c. predictor variable
d. response variable

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Definition of the Simple Regression Model
BUSPROG:
Feedback: A dependent variable is known as a response variable.

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2. If a change in variable x causes a change in variable y, variable x is called the

_____.
a. dependent variable
b. explained variable
c. explanatory variable
d. response variable

Answer: c
Difficulty: Easy
Bloom’s: Comprehension
A-Head: Definition of the Simple Regression Model
BUSPROG:
Feedback: If a change in variable x causes a change in variable y, variable x is
called the independent variable or the explanatory variable.

3. In the equation y =
β0 + β 1 x + u, β 0 is the _____.

a. dependent variable
b. independent variable
c. slope parameter
d. intercept parameter

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Definition of the Simple Regression Model
BUSPROG:
Feedback: In the equation y =
β0 + β 1 x + u, β 0 is the intercept parameter.

4. In the equation y =
β0 + β 1 x + u, what is the estimated value of β 0 ?

a. ý− β^1 x́

b.
ý +β 1 x́

y
yi −´¿
¿
¿
c. (x i− x́)¿
n

∑¿
i=1
¿

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d. ∑ xy
i=1

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Deriving the Ordinary Least Squares Estimates
BUSPROG:
Feedback: The estimated value of
β 0 is ý− β^1 x́ .

5. In the equation c =
β0 + β 1 i + u, c denotes consumption and i denotes

income. What is the residual for the 5th observation if

c 5 =$500 and c^5 =$475?

a. $975
b. $300
c. $25
d. $50

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Deriving the Ordinary Least Squares Estimates
BUSPROG:
Feedback: The formula for calculating the residual for the i th observation is
u^i= y i −^
y i . In this case, the residual is u^5=c5 −^
c 5 =$500 -$475= $25.

6. What does the equation ^y = β^0 + ^

β1 x denote if the regression equation is y = β0
+ β1x1 + u?
a. The explained sum of squares
b. The total sum of squares
c. The sample regression function
d. The population regression function

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Deriving the Ordinary Least Squares Estimates
BUSPROG:

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Feedback: The equation ^y = β^0 + ^

β1 x denotes the sample regression function of the
given regression model.

7. Consider the following regression model: y = β 0 + β1x1 + u. Which of the following

is a property of Ordinary Least Square (OLS) estimates of this model and their
associated statistics?
a. The sum, and therefore the sample average of the OLS residuals, is positive.
b. The sum of the OLS residuals is negative.
c. The sample covariance between the regressors and the OLS residuals is positive.
d. The point ( x́ , ý ) always lies on the OLS regression line.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Properties of OLS on Any Sample of Data
BUSPROG:
Feedback: An important property of the OLS estimates is that the point ( x́ , ý )

always lies on the OLS regression line. In other words, if x=x́ , the predicted value

of y is ý .

8. The explained sum of squares for the regression function,

y i=β 0 +β 1 x 1+u1 , is

defined as _____.
n

a. ∑ ( y i− ý )2
i=1

b. ∑ ( y i−^y )2
i=1

c. ∑ u^i
i=1

d. ∑ (ui)2
i=1

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Properties of OLS on Any Sample of Data

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BUSPROG:
n

Feedback: The explained sum of squares is defined as ∑ ( y i−^y )2

i=1

9. If the total sum of squares (SST) in a regression equation is 81, and the residual
sum of squares (SSR) is 25, what is the explained sum of squares (SSE)?
a. 64
b. 56
c. 32
d. 18

Answer: b
Difficulty: Moderate
Bloom’s: Application
A-Head: Properties of OLS on Any Sample of Data
BUSPROG: Analytic
Feedback: Total sum of squares (SST) is given by the sum of explained sum of
squares (SSE) and residual sum of squares (SSR). Therefore, in this case, SSE=81-
25=56.

10. If the residual sum of squares (SSR) in a regression analysis is 66 and the total
sum of squares (SST) is equal to 90, what is the value of the coefficient of
determination?
a. 0.73
b. 0.55
c. 0.27
d. 1.2

Answer: c
Difficulty: Moderate
Bloom’s: Application
A-Head: Properties of OLS on Any Sample of Data
BUSPROG: Analytic
Feedback: The formula for calculating the coefficient of determination is
SSR 66
R2=1− . In this case,
R2=1− =0.27
SST 90

11. Which of the following is a nonlinear regression model?

a. y = β0 + β1x1/2 + u
b. log y = β0 + β1log x +u
c. y = 1 / (β0 + β1x) + u
d. y = β0 + β1x + u

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Answer: c
Difficulty: Moderate
Bloom’s: Comprehension
A-Head: Properties of OLS on Any Sample of Data
BUSPROG:
Feedback: A regression model is nonlinear if the equation is nonlinear in the
parameters. In this case, y=1 / (β0 + β1x) + u is nonlinear as it is nonlinear in its
parameters.

12. Which of the following is assumed for establishing the unbiasedness of Ordinary
Least Square (OLS) estimates?
a. The error term has an expected value of 1 given any value of the explanatory
variable.
b. The regression equation is linear in the explained and explanatory variables.
c. The sample outcomes on the explanatory variable are all the same value.
d. The error term has the same variance given any value of the explanatory
variable.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Expected Values and Variances of the OLS Estimators
BUSPROG:
Feedback: The error u has the same variance given any value of the explanatory
variable.

13. The error term in a regression equation is said to exhibit homoskedasticty if

_____.
a. it has zero conditional mean
b. it has the same variance for all values of the explanatory variable.
c. it has the same value for all values of the explanatory variable
d. if the error term has a value of one given any value of the explanatory variable.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Expected Values and Variances of the OLS Estimators
BUSPROG:
Feedback: The error term in a regression equation is said to exhibit homoskedasticty
if it has the same variance for all values of the explanatory variable.

14. In the regression of y on x, the error term exhibits heteroskedasticity if _____.

a. it has a constant variance

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b. Var(y|x) is a function of x
c. x is a function of y
d. y is a function of x

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Expected Values and Variances of the OLS Estimators
BUSPROG:
Feedback: Heteroskedasticity is present whenever Var(y|x) is a function of x
because Var(u|x) = Var(y|x).

15. What is the estimated value of the slope parameter when the regression
equation, y = β0 + β1x1 + u passes through the origin?
n

a. ∑ yi
i=1

y
¿
¿
b. ¿ )
n

∑¿
i=1

∑ xi yi
i=1
c. n

∑ x i2
i=1

d. ∑ ( y i− ý )2
i=1

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Regression through the Origin and Regression on a Constant
BUSPROG:

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Feedback: The estimated value of the slope parameter when the regression
n

∑ xi yi
i=1
equation passes through the origin is n .
∑ x i2
i=1

16. A natural measure of the association between two random variables is the
correlation coefficient.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Definition of the Simple Regression Model
BUSPROG:
Feedback: A natural measure of the association between two random variables is
the correlation coefficient.

17. The sample covariance between the regressors and the Ordinary Least Square
(OLS) residuals is always positive.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Properties of OLS on Any Sample of Data
BUSPROG:
Feedback: The sample covariance between the regressors and the Ordinary Least
Square (OLS) residuals is zero.

18. R2 is the ratio of the explained variation compared to the total variation.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Properties of OLS on Any Sample of Data
BUSPROG:
Feedback: The sample covariance between the regressors and the Ordinary Least
Square (OLS) residuals is zero.

19. There are n-1 degrees of freedom in Ordinary Least Square residuals.

Answer: False

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Difficulty: Easy
Bloom’s: Knowledge
A-Head: Expected Values and Variances of the OLS Estimators
BUSPROG:
Feedback: There are n-2 degrees of freedom in Ordinary Least Square residuals.

20. The variance of the slope estimator increases as the error variance decreases.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Expected Values and Variances of the OLS Estimators
BUSPROG:
Feedback: The variance of the slope estimator increases as the error variance
increases.

Chapter 3

1. In the equation,
y=β 0 +β 1 x 1 + β 2 x 2+ u , β 2 is a(n) _____.

a. independent variable
b. dependent variable
c. slope parameter
d. intercept parameter

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Motivation for Multiple Regression
BUSPROG:
Feedback: In the equation,
y=β 0 + β 1 x 1 + β 2 x 2+u , β 2 is a slope parameter.

2. Consider the following regression equation:

y=β 1 +β 2 x 1+ β2 x 2+u . What does β
1

imply?
a.
β 1 measures the ceteris paribus effect of x 1 on x2 .

b.
β 1 measures the ceteris paribus effect of y on x1 .

c.
β 1 measures the ceteris paribus effect of x 1 on y .

d.
β 1 measures the ceteris paribus effect of x 1 on u .

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Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Motivation for Multiple Regression
BUSPROG:
Feedback:
β 1 measures the ceteris paribus effect of x 1 on y .

3. If the explained sum of squares is 35 and the total sum of squares is 49, what is
the residual sum of squares?
a. 10
b. 12
c. 18
d. 14

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Mechanics and Interpretation of Ordinary Least Squares
BUSPROG: Analytic
Feedback: The residual sum of squares is obtained by subtracting the explained
sum of squares from the total sum of squares, or 49-35=14.

4. Which of the following is true of R2?

a. R2 is also called the standard error of regression.
b. A low R2 indicates that the Ordinary Least Squares line fits the data well.
c. R2 usually decreases with an increase in the number of independent variables in a
regression.
d. R2 shows what percentage of the total variation in the dependent variable, Y, is
explained by the explanatory variables.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Mechanics and Interpretation of Ordinary Least Squares
BUSPROG:
Feedback: R2 shows what percentage of the total variation in Y is explained by the
explanatory variables.

5. The value of R2 always _____.

a. lies below 0
b. lies above 1
c. lies between 0 and 1

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d. lies between 1 and 1.5

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Mechanics and Interpretation of Ordinary Least Squares
BUSPROG:
Feedback: By definition, the value of R2 always lies between 0 and 1.

6. If an independent variable in a multiple linear regression model is an exact linear

combination of other independent variables, the model suffers from the problem of
_____.
a. perfect collinearity
b. homoskedasticity
c. heteroskedasticty
d. omitted variable bias

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Expected Value of the OLS Estimators
BUSPROG:
Feedback: If an independent variable in a multiple linear regression model is an
exact linear combination of other independent variables, the model suffers from the
problem of perfect collinearity.

7. The assumption that there are no exact linear relationships among the
independent variables in a multiple linear regression model fails if _____, where n is
the sample size and k is the number of parameters.
a. n>2
b. n=k+1
c. n>k
d. n<k+1

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Expected Value of the OLS Estimators
BUSPROG:
Feedback: The assumption of no perfect collinearity among independent variables
fails if n<k+1.

8. Exclusion of a relevant variable from a multiple linear regression model leads to

the problem of _____.

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a. misspecification of the model

b. multicollinearity
c. perfect collinearity
d. homoskedasticity

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Expected Value of the OLS Estimators
BUSPROG:
Feedback: Exclusion of a relevant variable from a multiple linear regression model
leads to the problem of misspecification of the model.

9. Suppose the variable x2 has been omitted from the following regression equation,
~
y=β 0 + β 1 x 1 +β 2 x 2+ u . β 1 is the estimator obtained when x is omitted from the
2

~
equation. The bias in β 1 is positive if _____.

a.
β 2 >0 and x and x are positively correlated
1 2

b.
β 2 <0 and x and x are positively correlated
1 2

c.
β 2 >0 and x and x are negatively correlated
1 2

d.
β 2 = 0 and x and x are negatively correlated
1 2

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Expected Value of the OLS Estimators
BUSPROG:
~
Feedback: When the variable x2 is omitted from the regression, the bias in β 1 is

positive if
β 2 >0 and x and x are positively correlated.
1 2

10. Suppose the variable x2 has been omitted from the following regression
~
equation,
y=β 0 +β 1 x 1 + β 2 x 2+ u . β 1 is the estimator obtained when x is omitted
2

~
from the equation. The bias in β 1 is negative if _____.

a.
β 2 >0 and x and x are positively correlated
1 2

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b.
β 2 <0 and x and x are positively correlated
1 2

c.
β 2 =0 and x and x are negatively correlated
1 2

d.
β 2 =0 and x and x are negatively correlated
1 2

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Expected Value of the OLS Estimators
BUSPROG:
~
Feedback: When the variable x2 is omitted from the regression, the bias in β 1 is

negative if
β 2 <0 and x and x are positively correlated.
1 2

11. Suppose the variable x2 has been omitted from the following regression
~
equation,
y=β 0 +β 1 x 1 + β 2 x 2+ u . β 1 is the estimator obtained when x is omitted
2

~ ~
from the equation. If E( β 1 ) >β1, β 1 is said to _____.

a. have an upward bias

b. have a downward bias
c. be unbiased
d. be biased toward zero

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Expected Value of the OLS Estimators
BUSPROG:
Feedback: When the variable x2 is omitted from the following regression equation,
~ ~
y=β 0 + β 1 x 1 +β 2 x 2+ u , , β 1 has an upward bias if E( β 1 ) >β .
1

12. High (but not perfect) correlation between two or more independent variables is
called _____.
a. heteroskedasticty
b. homoskedasticty
c. multicollinearity
d. micronumerosity

Answer: c

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Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Variance of the OLS Estimators
BUSPROG:
Feedback: High, but not perfect, correlation between two or more independent
variables is called multicollinearity.

13. The term _____ refers to the problem of small sample size.
a. micronumerosity
b. multicollinearity
c. homoskedasticity
d. heteroskedasticity

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Variance of the OLS Estimators
BUSPROG:
Feedback: The term micronumerosity refers to the problem of small sample size.

14. Find the degrees of freedom in a regression model that has 10 observations and
7 independent variables.
a. 17
b. 2
c. 3
d. 4

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Variance of the OLS Estimators
BUSPROG: Analytic
Feedback: The degrees of freedom in a regression model is computed by
subtracting the number of parameters from the number of observations in a
regression model. Since, the number of parameters is one more than the number of
independent variables, the degrees of freedom in this case is 10-(7 + 1) = 2.

15. The Gauss-Markov theorem will not hold if _____.

a. the error term has the same variance given any values of the explanatory
variables
b. the error term has an expected value of zero given any values of the independent
variables
c. the independent variables have exact linear relationships among them

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d. the regression model relies on the method of random sampling for collection of
data

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Efficiency of OLS: The Gauss-Markov Theorem
BUSPROG:
Feedback: The Gauss-Markov theorem will not hold if the independent variables
have exact linear relationships among them.

16. The term “linear” in a multiple linear regression model means that the equation
is linear in parameters.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Motivation for Multiple Regression
BUSPROG:
Feedback: The term “linear” in a multiple linear regression model means that the
equation is linear in parameters.

17. The key assumption for the general multiple regression model is that all factors
in the unobserved error term be correlated with the explanatory variables.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Motivation for Multiple Regression
BUSPROG:
Feedback: The key assumption of the general multiple regression model is that all
factors in the unobserved error term be uncorrelated with the explanatory variables.

18. The coefficient of determination (R2) decreases when an independent variable is

added to a multiple regression model.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Mechanics and Interpretation of Ordinary Least Squares
BUSPROG:
Feedback: The coefficient of determination (R2) never decreases when an
independent variable is added to a multiple regression model.

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19. An explanatory variable is said to be exogenous if it is correlated with the error

term.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Expected Value of the OLS Estimators
BUSPROG:
Feedback: An explanatory variable is said to be endogenous if it is correlated with
the error term.

20. A larger error variance makes it difficult to estimate the partial effect of any of
the independent variables on the dependent variable.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Variance of the OLS Estimators
BUSPROG:
Feedback: A larger error variance makes it difficult to estimate the partial effect of
any of the independent variables on the dependent variable.

Chapter 4

1. The normality assumption implies that:

a. the population error u is dependent on the explanatory variables and is normally
distributed with mean equal to one and variance σ 2.
b. the population error u is independent of the explanatory variables and is normally
distributed with mean equal to one and variance σ.
c. the population error u is dependent on the explanatory variables and is normally
distributed with mean zero and variance σ.
d. the population error u is independent of the explanatory variables and is normally
distributed with mean zero and variance σ 2.

Answer: d
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Sampling Distributions of the OLS Estimators
BUSPROG:
Feedback: The normality assumption implies that the population error ‘u’ is
independent of the explanatory variables and is normally distributed with mean
zero and variance σ2.

2. Which of the following statements is true?

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a. Taking a log of a nonnormal distribution yields a distribution that is closer to

normal.
b. The mean of a nonnormal distribution is 0 and the variance is σ 2.
c. The CLT assumes that the dependent variable is unaffected by unobserved
factors.
d. OLS estimators have the highest variance among unbiased estimators.

Answer: a
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Sampling Distribution of the OLS Estimators
BUSPROG:
Feedback: Transformations such as logs of nonnormal distributions, yields
distributions which are closer to normal.

3. A normal variable is standardized by:

a. subtracting off its mean from it and multiplying by its standard deviation.
b. adding its mean to it and multiplying by its standard deviation.
c. subtracting off its mean from it and dividing by its standard deviation.
d. adding its mean to it and dividing by its standard deviation.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Sampling Distribution of the OLS Estimators
BUSPROG:
Feedback: A normal variable is standardized by subtracting off its mean from it and
dividing by its standard deviation.

4. Which of the following is a statistic that can be used to test hypotheses about a
single population parameter?
a. F statistic
b. t statistic
c. χ2 statistic
d. Durbin Watson statistic

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing Hypotheses about a Single Population Parameter: The t Test
BUSPROG:
Feedback: The t statistic can be used to test hypotheses about a single population
parameter.

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5. Consider the equation, Y = β 1 + β2X2 + u. A null hypothesis, H0: β2 = 0 states that:

a. X2 has no effect on the expected value of β2.
b. X2 has no effect on the expected value of Y.
c. β2 has no effect on the expected value of Y.
d. Y has no effect on the expected value of X 2.

Answer: b
Difficulty: Moderate
Bloom’s: Comprehension
A-Head: Testing Hypotheses about a Single Population Parameter: The t Test
BUSPROG:
Feedback: In such an equation, a null hypothesis, H 0: β2 = 0 states that X2 has no
effect on the expected value of Y. This is because β 2 is the coefficient associated
with X2.

6. The significance level of a test is:

a. the probability of rejecting the null hypothesis when it is false.
b. one minus the probability of rejecting the null hypothesis when it is false.
c. the probability of rejecting the null hypothesis when it is true.
d. one minus the probability of rejecting the null hypothesis when it is true.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Testing Hypotheses about a Single Population Parameter: The t Test
BUSPROG:
Feedback: The significance level of a test refers to the probability of rejecting the
null hypothesis when it is in fact true.

7. The general t statistic can be written as:

Hypothesized value
a. t = Standard e rror

estimate – hypothesized value

b. t = ¿ )

(estimate – hypothesized value)

c. t = variance

(estimate – hypothesized value)

d. t = standard error

Answer: d
Difficulty: Easy

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Bloom’s: Knowledge
A-Head: Testing Hypotheses about a Single Population Parameter: The t Test
BUSPROG:
Feedback: The general t statistic can be written as t = (estimate – hypothesized
value)/standard error.

8. Which of the following statements is true of confidence intervals?

a. Confidence intervals in a CLM are also referred to as point estimates.
b. Confidence intervals in a CLM provide a range of likely values for the population
parameter.
c. Confidence intervals in a CLM do not depend on the degrees of freedom of a
distribution.
d. Confidence intervals in a CLM can be truly estimated when heteroskedasticity is
present.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Confidence Intervals
BUSPROG:
Feedback: Confidence intervals provide a range of likely values for the population
parameter and are not point estimates. Estimation of confidence intervals depends
on the degrees of freedom of the distribution and cannot be truly estimated when
heteroskedasticity is present.

9. Which of the following statements is true?

a. When the standard error of an estimate increases, the confidence interval for the
estimate narrows down.
b. Standard error of an estimate does not affect the confidence interval for the
estimate.
c. The lower bound of the confidence interval for a regression coefficient, say β j, is
^β ^
given by J - [standard error × ( β J )]
d. The upper bound of the confidence interval for a regression coefficient, say βj, is
^β ^
given by J + [Critical value × standard error ( β J )]

Answer: d
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Confidence Intervals
BUSPROG:

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Feedback: The upper bound of the confidence interval for a regression coefficient,
^β ^
say βj, is given by J + [Critical value × standard error ( β J )].

10. Which of the following tools is used to test multiple linear restrictions?
a. t test
b. z test
c. F test
d. Unit root test

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing Multiple Linear Restrictions: The F test
BUSPROG:
Feedback: The F test is used to test multiple linear restrictions.

11. Which of the following statements is true of hypothesis testing?

a. The t test can be used to test multiple linear restrictions.
b. A test of single restriction is also referred to as a joint hypotheses test.
c. A restricted model will always have fewer parameters than its unrestricted model.
d. OLS estimates maximize the sum of squared residuals.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Testing Multiple Linear Restrictions: The F test
BUSPROG:
Feedback: A restricted model will always have fewer parameters than its
unrestricted model.

12. Which of the following correctly defines F statistic if SSR r represents sum of
squared residuals from the restricted model of hypothesis testing, SSR ur represents
sum of squared residuals of the unrestricted model, and q is the number of
restrictions placed?
(SSR ur −SSR r )/q
a. F = SSR ur /( n−k −1)

(SSR r −SSRur )/q

b. F = SSR ur /(n−k −1)

(SSR ur −SSR r )/q

c. F = SSR r /( n−k−1)

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(SSR ur −SSR r )/(n−k−1)

d. F = SSR ur /q

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Testing Multiple Linear Restrictions: The F test
BUSPROG:
(SSR r −SSRur )/q
Feedback: The F statistic is given by, F = SSR ur /(n−k −1)

13. Which of the following statements is true?

a. If the calculated value of F statistic is higher than the critical value, we reject the
alternative hypothesis in favor of the null hypothesis.
b. The F statistic is always nonnegative as SSR r is never smaller than SSRur.
c. Degrees of freedom of a restricted model is always less than the degrees of
freedom of an unrestricted model.
d. The F statistic is more flexible than the t statistic to test a hypothesis with a
single restriction.

Answer: b
Difficulty: Moderate
Bloom’s: Comprehension
A-Head: Testing Multiple Linear Restrictions: The F test
BUSPROG:
Feedback: The F statistic is always nonnegative as SSRr is never smaller than SSRur.

14. If R2ur = 0.6873, R2r = 0.5377, number of restrictions = 3, and n – k – 1 = 229, F

statistic equals:
a. 21.2
b. 28.6
c. 36.5
d. 42.1

Answer: c
Difficulty: Hard
Bloom’s: Application
A-Head: Testing Multiple Linear Restrictions: The F test
BUSPROG: Analytic
Feedback: The F statistic can be calculated as F = [(R 2ur – R2r)/q] / [(1-R2ur)/n – k – 1].
Here, q represents the number of restrictions. In this case it is equal to [(0.6873 –
0.5377)/3] / [(1 – 0.6873)/229] = [0.04986/0.001365] = 36.5.

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15. Which of the following correctly identifies a reason why some authors prefer to
report the standard errors rather than the t statistic?
a. Having standard errors makes it easier to compute confidence intervals.
b. Standard errors are always positive.
c. The F statistic can be reported just by looking at the standard errors.
d. Standard errors can be used directly to test multiple linear regressions.

Answer: a
Difficulty: Medium
Bloom’s: Comprehension
A-Head: Reporting Regression Results
BUSPROG:
Feedback: One of the advantages of reporting standard errors over t statistics is
that confidence intervals can be easily calculated using stand errors.

16. Whenever the dependent variable takes on just a few values it is close to a
normal distribution.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Sampling Distribution of the OLS Estimators
BUSPROG:
Feedback: Whenever the dependent variable takes on just a few values it cannot
have anything close to a normal distribution. A normal distribution requires the
dependent variable to take up a large range of values.

17. If the calculated value of the t statistic is greater than the critical value, the null
hypothesis, H0 is rejected in favor of the alternative hypothesis, H1.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing Hypotheses about a Single Population Parameter: The t Test
BUSPROG:
Feedback: If the calculated value of the t statistic is greater than the critical value,
H0 is rejected in favor of H1.

18. H1: βj ≠ 0, where βj is a regression coefficient associated with an explanatory

variable, represents a one-sided alternative hypothesis.

Answer: False
Difficulty: Easy

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Bloom’s: Knowledge
A-Head: Testing Hypotheses about a Single Population Parameter: The t Test
BUSPROG:
Feedback: H1: βj ≠ 0, where βj is a regression coefficient associated with an
explanatory variable, represents a two-sided alternative hypothesis.

^ ^β
19. If β 1 and 2 are estimated values of regression coefficients associated with
^
two explanatory variables in a regression equation, then the standard error ( β 1 –
^β ^ ^
) = standard error ( β
2 1 ) – standard error ( β 2 ).

Answer: False
Difficulty: Easy
Bloom’s: Comprehension
A-Head: Testing Hypotheses about a Single Linear Combinations of the Parameters
BUSPROG:
^ ^β
Feedback: If β 1 and 2 are estimated values of regression coefficients
associated with two explanatory variables in a regression equation, then the
^ ^β ^ ^
standard error ( β 1 – 2 ) ≠ standard error ( β ) – standard error ( β
1 ).
2

20. Standard errors must always be positive.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing Hypotheses about a Single Linear Combinations of the Parameters
BUSPROG:
Feedback: Standard errors must always be positive since they are estimates of
standard deviations.
Chapter 5

1. Which of the following statements is true?

a. The standard error of a regression, σ^ , is not an unbiased estimator for σ ,

the standard deviation of the error, u, in a multiple regression model.

b. In time series regressions, OLS estimators are always unbiased.
c. Almost all economists agree that unbiasedness is a minimal requirement for an
estimator in regression analysis.
d. All estimators in a regression model that are consistent are also unbiased.

Answer: b

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Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Consistency
BUSPROG:
Feedback: The standard error of a regression is not an unbiased estimator for the
standard deviation of the error in a multiple regression model.

^β β , is consistent, then the:

2. If j, an unbiased estimator of j

^β β
a. distribution of j becomes more and more loosely distributed around j as
the sample size grows.
^β β
b. distribution of j becomes more and more tightly distributed around j as
the sample size grows.
^β
c. distribution of j tends toward a standard normal distribution as the sample
size grows.
^β
d. distribution of j remains unaffected as the sample size grows.

Answer: b
Difficulty: Medium
Bloom’s: Knowledge
A-Head: Consistency
BUSPROG:
^β β , is consistent, then the distribution
Feedback: If j, an unbiased estimator of j

^β β
of j becomes more and more tightly distributed around j as the sample size
grows.

^β β j, is also a consistent estimator of β j,

3. If j, an unbiased estimator of

then when the sample size tends to infinity:

^β
a. the distribution of j collapses to a single value of zero.
^β
b. the distribution of j diverges away from a single value of zero.
^β β j.
c. the distribution of j collapses to the single point
^β β j.
d. the distribution of j diverges away from

Answer: c
Difficulty: Easy

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Bloom’s: Knowledge
A-Head: Consistency
BUSPROG:
^β β , is also a consistent estimator of
Feedback: If j, an unbiased estimator of j

β j, then when the sample size tends to infinity the distribution of ^β

j collapses

to the single point β j.

4. In a multiple regression model, the OLS estimator is consistent if:

a. there is no correlation between the dependent variables and the error term.
b. there is a perfect correlation between the dependent variables and the error
term.
c. the sample size is less than the number of parameters in the model.
d. there is no correlation between the independent variables and the error term.

Answer: d
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Consistency
BUSPROG:
Feedback: In a multiple regression model, the OLS estimator is consistent if there is
no correlation between the explanatory variables and the error term.

5. If the error term is correlated with any of the independent variables, the OLS
estimators are:
a. biased and consistent.
b. unbiased and inconsistent.
c. biased and inconsistent.
d. unbiased and consistent.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Consistency
BUSPROG:
Feedback: If the error term is correlated with any of the independent variables, then
the OLS estimators are biased and inconsistent.

6. If δ1 = Cov(x1/x2) / Var(x1) where x1 and x2 are two independent variables in a

regression equation, which of the following statements is true?

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a. If x2 has a positive partial effect on the dependent variable, and δ 1 > 0, then the
inconsistency in the simple regression slope estimator associated with x 1 is
negative.
b. If x2 has a positive partial effect on the dependent variable, and δ 1 > 0, then the
inconsistency in the simple regression slope estimator associated with x 1 is positive.
c. If x1 has a positive partial effect on the dependent variable, and δ 1 > 0, then the
inconsistency in the simple regression slope estimator associated with x 1 is
negative.
d. If x1 has a positive partial effect on the dependent variable, and δ 1 > 0, then the
inconsistency in the simple regression slope estimator associated with x 1 is positive.

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Consistency
BUSPROG:
Feedback: Given that δ1 = Cov(x1/x2)/Var(x1) where x1 and x2 are two independent
variables in a regression equation, if x2 has a positive partial effect on the
dependent variable, and δ1 > 0, then the inconsistency in the simple regression
slope estimator associated with x1 is positive.

7. If OLS estimators satisfy asymptotic normality, it implies that:

a. they are approximately normally distributed in large enough sample sizes.
b. they are approximately normally distributed in samples with less than 10
observations.
c. they have a constant mean equal to zero and variance equal to σ 2.
d. they have a constant mean equal to one and variance equal to σ.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Asymptotic Normality and Large Sample Inference
BUSPROG:
Feedback: If OLS estimators satisfy asymptotic normality, it implies that they are
approximately normally distributed in large enough sample sizes.

8. In a regression model, if variance of the dependent variable, y, conditional on an

explanatory variable, x, or Var(y|x), is not constant, _____.
a. the t statistics are invalid and confidence intervals are valid for small sample
sizes
b. the t statistics are valid and confidence intervals are invalid for small sample
sizes
c. the t statistics confidence intervals are valid no matter how large the sample size
is

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c. the t statistics and confidence intervals are both invalid no matter how large the
sample size is

Answer: d
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Asymptotic Normality and Large Sample Inference
BUSPROG:
Feedback: If variance of the dependent variable conditional on an explanatory
variable is not a constant the usual t statistics confidence intervals are both invalid
no matter how large the sample size is.

^β
9. If j is an OLS estimator of a regression coefficient associated with one of the
^β
explanatory variables, such that j= 1, 2, …., n, asymptotic standard error of j

will refer to the:

^β
a. estimated variance of j when the error term is normally distributed.
b. estimated variance of a given coefficient when the error term is not normally
distributed.
^β
c. square root of the estimated variance of j when the error term is normally
distributed.
^β
d. square root of the estimated variance of j when the error term is not normally
distributed.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Asymptotic Normality and Large Sample Inference
BUSPROG:
Feedback: Asymptotic standard error refers to the square root of the estimated
^β
variance of j when the error term is not normally distributed.

10. A useful rule of thumb is that standard errors are expected to shrink at a rate
that is the inverse of the:
a. square root of the sample size.
b. product of the sample size and the number of parameters in the model.
c. square of the sample size.
d. sum of the sample size and the number of parameters in the model.

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Answer: a
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Asymptotic Normality and Large Sample Inference
BUSPROG:
Feedback: Standard errors can be expected to shrink at a rate that is the inverse of
the square root of the sample size.

11. An auxiliary regression refers to a regression that is used:

a. when the dependent variables are qualitative in nature.
b. when the independent variables are qualitative in nature.
c. to compute a test statistic but whose coefficients are not of direct interest.
d. to compute coefficients which are of direct interest in the analysis.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Asymptotic Normality and Large Sample Inference
BUSPROG:
Feedback: An auxiliary regression refers to a regression that is used to compute a
test statistic but whose coefficients are not of direct interest.

12. The n-R-squared statistic also refers to the:

a. F statistic.
b. t statistic.
c. z statistic.
d. LM statistic.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Asymptotic Normality and Large Sample Inference
BUSPROG:
Feedback: The n-R-squared statistic also refers to the LM statistic.

13. The LM statistic follows a:

a. t distribution.
b. f distribution.
c. χ 2
distribution.
d. binomial distribution.

Answer: c
Difficulty: Easy

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Bloom’s: Knowledge
A-Head: Asymptotic Normality and Large Sample Inference
BUSPROG:
Feedback: The LM statistic follows a χ 2
distribution.

14. Which of the following statements is true?

a. In large samples there are not many discrepancies between the outcomes of the
F test and the LM test.
b. Degrees of freedom of the unrestricted model are necessary for using the LM
test.
c. The LM test can be used to test hypotheses with single restrictions only and
provides inefficient results for multiple restrictions.
d. The LM statistic is derived on the basis of the normality assumption.

Answer: a
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Asymptotic Normality and Large Sample Inference
BUSPROG:
Feedback: In large samples there are not many discrepancies between the F test
and the LM test because asymptotically the two statistics have the same probability
of a Type 1 error.

15. Which of the following statements is true under the Gauss-Markov assumptions?
a. Among a certain class of estimators, OLS estimators are best linear unbiased, but
are asymptotically inefficient.
b. Among a certain class of estimators, OLS estimators are biased but
asymptotically efficient.
c. Among a certain class of estimators, OLS estimators are best linear unbiased and
asymptotically efficient.
d. The LM test is independent of the Gauss-Markov assumptions.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Asymptotic Efficiency of OLS
BUSPROG:
Feedback: Under the Gauss-Markov assumptions, among a certain class of
estimators, OLS estimators are best linear unbiased and asymptotically efficient.

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16. If variance of an independent variable in a regression model, say x 1, is greater

^β
than 0, or Var(x1) > 0, the inconsistency in 1 (estimator associated with x1) is
negative, if x1 and the error term are positively related.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Consistency
BUSPROG:
Feedback: If variance of an independent variable, say x 1, is greater than 0, the
^β
inconsistency in 1 (estimator associated with x1) is positive if x1 and the error
term are positively related.

17. Even if the error terms in a regression equation, u 1, u2,….., un, are not normally
distributed, the estimated coefficients can be normally distributed.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Asymptotic Normality and Large Sample Inference
BUSPROG:
Feedback: Even if the error terms in a regression equation, u 1, u2,….., un, are not
normally distributed, the estimated coefficients cannot be normally distributed.

18. A normally distributed random variable is symmetrically distributed about its

mean, it can take on any positive or negative value (but with zero probability), and
more than 95% of the area under the distribution is within two standard deviations.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Asymptotic Normality and Large Sample Inference
BUSPROG:
Feedback: A normally distributed random variable is symmetrically distributed about
its mean, it can take on any positive or negative value (but with zero probability),
and more than 95% of the area under the distribution is within two standard
deviations.

19. The F statistic is also referred to as the score statistic.

Answer: False
Difficulty: Easy

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Bloom’s: Knowledge
A-Head: Asymptotic Normality and Large Sample Inference
BUSPROG:
Feedback: The LM statistic is also referred to as the score statistic.

20. The LM statistic requires estimation of the unrestricted model only.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Asymptotic Normality and Large Sample Inference
BUSPROG:
Feedback: The LM statistic requires estimation of the restricted model only.
Chapter 6

1. A change in the unit of measurement of the dependent variable in a model does

not lead to a change in:
a. the standard error of the regression.
b. the sum of squared residuals of the regression.
c. the goodness-of-fit of the regression.
d. the confidence intervals of the regression.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Effects of Data Scaling on OLS Statistics
BUSPROG:
Feedback: Changing the unit of measurement of the dependent variable in a model
does not lead to a change in the goodness of fit of the regression.

2. Changing the unit of measurement of any independent variable, where log of the
dependent variable appears in the regression:
a. affects only the intercept coefficient.
b. affects only the slope coefficient.
c. affects both the slope and intercept coefficients.
d. affects neither the slope nor the intercept coefficient.

Answer: a
Difficulty: Moderate
Bloom’s: Comprehension
A-Head: Effects of Data Scaling on OLS Statistics
BUSPROG:

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Feedback: Changing the unit of measurement of any independent variable, where

log of the independent variable appears in the regression only affects the intercept.
This follows from the property log(ab) = log(a) + log(b).

3. A variable is standardized in the sample:

a. by multiplying by its mean.
b. by subtracting off its mean and multiplying by its standard deviation.
c. by subtracting off its mean and dividing by its standard deviation.
d. by multiplying by its standard deviation.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Effects of Data Scaling on OLS Statistics
BUSPROG:
Feedback: A variable is standardized in the sample by subtracting off its mean and
dividing by its standard deviation.

4. Standardized coefficients are also referred to as:

a. beta coefficients.
b. y coefficients.
c. alpha coefficients.
d. j coefficients.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Effects of Data Scaling on OLS Statistics
BUSPROG:
Feedback: Standardized coefficients are also referred to as beta coefficients.

5. If a regression equation has only one explanatory variable, say x 1, its

standardized coefficient must lie in the range:
a. -2 to 0.
b. -1 to 1.
c. 0 to 1.
d. 0 to 2.

Answer: b
Difficulty: Easy
Bloom’s: Comprehension
A-Head: Effects of Data Scaling on OLS Statistics
BUSPROG:

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Feedback: If a regression equation has only one explanatory variable, say x 1, its
standardized coefficient is the correlation coefficient between the dependent
variable and x1, and must lie in the range -1 to 1.

6. In the following equation, gdp refers to gross domestic product, and FDI refers to
foreign direct investment.

log(gdp) = 2.65 + 0.527log(bankcredit) + 0.222FDI

(0.13) (0.022) (0.017)

Which of the following statements is then true?

a. If gdp increases by 1%, bank credit increases by 0.527%, the level of FDI
remaining constant.
b. If bank credit increases by 1%, gdp increases by 0.527%, the level of FDI
remaining constant.
c. If gdp increases by 1%, bank credit increases by log(0.527)%, the level of FDI
remaining constant.
d. If bank credit increases by 1%, gdp increases by log(0.527)%, the level of FDI
remaining constant.

Answer: b
Difficulty: Moderate
Bloom’s: Application
A-Head: More on Functional Form
BUSPROG:
Feedback: The equation suggests that if bank credit increases by 1%, gdp increases
by 0.527%. This is known from the value of the coefficient associated with bank
credit.

7. In the following equation, gdp refers to gross domestic product, and FDI refers to
foreign direct investment.
log(gdp) = 2.65 + 0.527log(bankcredit) + 0.222FDI
(0.13) (0.022) (0.017)

Which of the following statements is then true?

a. If FDI increases by 1%, gdp increases by approximately 22.2%, the amount of
bank credit remaining constant.
b. If FDI increases by 1%, gdp increases by approximately 26.5%, the amount of
bank credit remaining constant.
c. If FDI increases by 1%, gdp increases by approximately 24.8%, the amount of
bank credit remaining constant.
d. If FDI increases by 1%, gdp increases by approximately 52.7%, the amount of
bank credit remaining constant.

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Answer: c
Difficulty: Hard
Bloom’s: Application
A-Head: More on Functional Form
BUSPROG:
Feedback: The equation suggests that if FDI increases by 1%, gdp increases by
100(exp(0.222) – 1)%. This equals (1.24857 -1) = 24.8% approx.

8. Which of the following statements is true when the dependent variable, y > 0?
a. Taking log of a variable often expands its range.
b. Models using log(y) as the dependent variable will satisfy CLM assumptions more
closely than models using the level of y.
c. Taking log of variables make OLS estimates more sensitive to extreme values.
d. Taking logarithmic form of variables make the slope coefficients more responsive
to rescaling.

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: More on Functional Form
BUSPROG:
Feedback: Models using log(y) as the dependent variable will satisfy CLM
assumptions more closely than models using the level of y. This is because taking
log of a variable gets it closer to a normal distribution.

9. Which of the following correctly identifies a limitation of logarithmic

transformation of variables?
a. Taking log of variables make OLS estimates more sensitive to extreme values in
comparison to variables taken in level.
b. Logarithmic transformations cannot be used if a variable takes on zero or
negative values.
c. Logarithmic transformations of variables are likely to lead to heteroskedasticity.
d. Taking log of a variable often expands its range which can cause inefficient
estimates.

Answer: b
Difficulty: Moderate
Bloom’s: Comprehension
A-Head: More on Functional Form
BUSPROG:
Feedback: Logarithmic transformations cannot be used if a variable takes on zero or
negative values.

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10. Which of the following models is used quite often to capture decreasing or
increasing marginal effects of a variable?
a. Models with logarithmic functions
b. Models with quadratic functions
c. Models with variables in level
d. Models with interaction terms

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: More on Functional Form
BUSPROG:
Feedback: Models with quadratic functions are used quite often to capture
decreasing or increasing marginal effects of a variable

11. Which of the following correctly represents the equation for adjusted R 2?
a. Ŕ 2
= 1 – [SSR/(n –1)]/[SST/(n+1)]

b. Ŕ 2
= 1 – [SSR/(n –k – 1)]/[SST/(n+1)]

c. Ŕ 2
= 1 – [SSR/(n –k – 1)]/[SST/(n – 1)]

d. Ŕ 2
= 1 – [SSR]/[SST/(n – 1)]

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: More on Goodness-of-Fit and Selection of Regressors
BUSPROG:
Feedback: Ŕ 2
= 1 – [SSR/(n –k – 1)]/[SST/(n – 1)]

12. Which of the following correctly identifies an advantage of using adjusted R 2

over R2?
a. Adjusted R2 corrects the bias in R2.
b. Adjusted R2 is easier to calculate than R2.
c. The penalty of adding new independent variables is better understood through
adjusted R2 than R2.
d. The adjusted R2 can be calculated for models having logarithmic functions while
R2 cannot be calculated for such models.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge

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A-Head: More on Goodness-of-Fit and Selection of Regressors

BUSPROG:
Feedback: The penalty of adding new independent variables is better understood
through adjusted R2 than R2 since its calculation is directly dependent on the
number of independent variables included.

13. Two equations form a nonnested model when:

a. one is logarithmic and the other is quadratic.
b. neither equation is a special case of the other.
c. each equation has the same independent variables.
d. there is only one independent variable in both equations.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: More on Goodness-of-Fit and Selection of Regressors
BUSPROG:
Feedback: Two equations form a nonnested model when neither equation is a
special case of the other.

14. A predicted value of a dependent variable:

a. represents the difference between the expected value of the dependent variable
and its actual value.
b. is always equal to the actual value of the dependent variable.
c. is independent of explanatory variables and can be estimated on the basis of the
residual error term only.
d. represents the expected value of the dependent variable given particular values
for the explanatory variables.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Prediction and Residual Analysis
BUSPROG:
Feedback: A predicted value of a dependent variable represents the expected value
of the dependent variable given particular values for the explanatory variables.

15. Residual analysis refers to the process of:

a. examining individual observations to see whether the actual value of a
dependent variable differs from the predicted value.
b. calculating the squared sum of residuals to draw inferences for the consistency of
estimates.

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c. transforming models with variables in level to logarithmic functions so as to

understand the effect of percentage changes in the independent variable on the
dependent variable.
d. sampling and collection of data in such a way to minimize the squared sum of
residuals.

Answer: a
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Prediction and Residual Analysis
BUSPROG:
Feedback: Residual analysis refers to the process of examining individual
observations to see whether the actual value of a dependent variable differs from
the predicted value.

16. Beta coefficients are always greater than standardized coefficients.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Effects of Data Scaling on OLS Statistics
BUSPROG:
Feedback: Beta coefficients the same as standardized coefficients.

17. If a new independent variable is added to a regression equation, the adjusted R 2

increases only if the absolute value of the t statistic of the new variable is greater
than one.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: More on Goodness-of-Fit and Selection of Regressors
BUSPROG:
Feedback: If a new independent variable is added to a regression equation, the
adjusted R2 increases only if the absolute value of the t statistic of the new variable
is greater than one in absolute value.

18. F statistic can be used to test nonnested models.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: More on Goodness-of-Fit and Selection of Regressors
BUSPROG:

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Feedback: F statistic can be used only to test nested models.

19. Predictions of a dependent variable are subject to sampling variation.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Prediction and Residual Analysis
BUSPROG:
Feedback: Predictions of a dependent variable are subject to sampling variation
since they are obtained using OLS estimators.

20. To make predictions of logarithmic dependent variables, they first have to be

converted to their level forms.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Prediction and Residual Analysis
BUSPROG:
Feedback: It is possible to make predictions of dependent variables when they are in
their logarithmic form. It is not necessary to convert them into their level forms.
Chapter 7

1. A _____ variable is used to incorporate qualitative information in a regression

model.
a. dependent
b. continuous
c. binomial
d. dummy

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Describing Qualitative Information
BUSPROG:
Feedback: A dummy variable or binary variable is used to incorporate qualitative
information in a regression model.

2. In a regression model, which of the following will be described using a binary

variable?
a. Whether it rained on a particular day or it did not
b. The volume of rainfall during a year
c. The percentage of humidity in air on a particular day

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d. The concentration of dust particles in air

Answer: a
Difficulty: Medium
Bloom’s: Comprehension
A-Head: Describing Qualitative Information
BUSPROG:
Feedback: A binary variable is used to describe qualitative information in regression
model. Therefore, such a variable will be used to describe whether it rained on a
particular day or it did not.

3. Which of the following is true of dummy variables?

a. A dummy variable always takes a value less than 1.
b. A dummy variable always takes a value higher than 1.
c. A dummy variable takes a value of 0 or 1.
d. A dummy variable takes a value of 1 or 10.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Describing Qualitative Information
BUSPROG:
Feedback: A dummy variable takes a value of 0 or 1.

The following simple model is used to determine the annual savings of an individual
on the basis of his annual income and education.
Savings = β0+∂0 Edu + β1Inc+u
The variable ‘Edu’ takes a value of 1 if the person is educated and the variable ‘Inc’
measures the income of the individual.

4. Refer to the model above. The inclusion of another binary variable in this model
that takes a value of 1 if a person is uneducated, will give rise to the problem of
_____.

a. omitted variable bias

b. self-selection
c. dummy variable trap
d. heteroskedastcity

Answer: c
Difficulty: Medium
Bloom’s: Application
A-Head: Describing Qualitative Information
BUSPROG: Analytic

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Feedback: The inclusion of another dummy variable in this model would introduce
perfect collinearity and lead to a dummy variable trap.

The following simple model is used to determine the annual savings of an individual
on the basis of his annual income and education.
Savings = β0+∂0 Edu + β1Inc+u
The variable ‘Edu’ takes a value of 1 if the person is educated and the variable ‘Inc’
measures the income of the individual.

5. Refer to the model above. The benchmark group in this model is _____.
a. the group of educated people
b. the group of uneducated people
c. the group of individuals with a high income
d. the group of individuals with a low income

Answer: b
Difficulty: Moderate
Bloom’s: Application
A-Head: A Single Dummy Independent Variable
BUSPROG: Analytic
Feedback: The benchmark group is the group against which comparisons are made.
In this case, the savings of a literate person is being compared to the savings of an
illiterate person; therefore, the group of illiterate people is the base group or
benchmark group.

The following simple model is used to determine the annual savings of an individual
on the basis of his annual income and education.
Savings = β0+∂0 Edu + β1Inc+u
The variable ‘Edu’ takes a value of 1 if the person is educated and the variable ‘Inc’
measures the income of the individual.

6. Refer to the above model. If ∂0 > 0, _____.

a. uneducated people have higher savings than those who are educated
b. educated people have higher savings than those who are not educated
c. individuals with lower income have higher savings
d. individual with lower income have higher savings

Answer: b
Difficulty: Moderate
Bloom’s: Application
A-Head: A Single Dummy Independent Variable
BUSPROG: Analytic

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Feedback: The coefficient ∂0 measures the impact of education on an individual’s

annual savings. If it has a positive impact, as in this case, educated people should
have higher savings.

7. The income of an individual in Budopia depends on his ethnicity and several other
factors which can be measured quantitatively. If there are 5 ethnic groups in
Budopia, how many dummy variables should be included in the regression equation
for income determination in Budopia?
a. 1
b. 5
c. 6
d. 4

Answer: d
Difficulty: Moderate
Bloom’s: Application
A-Head: Using Dummy Variables for Multiple Categories
BUSPROG: Analytic
Feedback: If a regression model is to have different intercepts for, say, g groups or
categories, we need to include g -1 dummy variables in the model along with an
intercept. In this case, the regression equation should include 5-1=4 dummy
variables since there are 5 ethnic groups.

8. The quarterly increase in an employee’s salary depends on the rating of his work
by his employer and several other factors as shown in the model below:
Increase in salary= β0+∂0Rating + other factors. The variable ‘Rating’ is a(n) _____
variable.
a. dependent variable
b. ordinal variable
c. continuous variable
d. Poisson variable

Answer: b
Difficulty: Moderate
Bloom’s: Application
A-Head: Using Dummy Variables for Multiple Categories
BUSPROG: Analytic
Feedback: The value of the variable ‘Rating’ depends on the employer’s rating of
the worker. Therefore, it incorporates ordinal information and is called an ordinal
variable.

9. Which of the following is true of Chow test?

a. It is a type of t test.
b. It is a type of sign test.

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c. It is only valid under homoskedasticty.

d. It is only valid under heteroskedasticity.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Interactions Involving Dummy Variables
BUSPROG:
Feedback: Since the Chow test is just an F test, it is only valid under
homoskedasticity.

10. Which of the following is true of dependent variables?

a. A dependent variable can only have a numerical value.
b. A dependent variable cannot have more than 2 values.
c. A dependent variable can be binary.
d. A dependent variable cannot have a qualitative meaning.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: A Binary Dependent Variable: The Linear Probability Model
BUSPROG:
Feedback: A dependent variable is binary if it has a qualitative meaning.

11. In the following regression equation, y is a binary variable:

y= β 0+β1x1+…βk xk+ u
^
In this case, the estimated slope coefficient, β 1 measures _____.
a. the predicted change in the value of y when x 1 increases by one unit, everything
else remaining constant
b. the predicted change in the value of y when x 1 decreases by one unit, everything
else remaining constant
c. the predicted change in the probability of success when x 1 decreases by one unit,
everything else remaining constant
d. the predicted change in the probability of success when x 1 increases by one unit,
everything else remaining constant

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: A Binary Dependent Variable: The Linear Probability Model
BUSPROG:
Feedback: A binary dependent variable is used when a regression model is used to
explain a qualitative event. The dependent variable takes a value of 1 when the

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event takes place (success) and it takes a value of zero when the event does not
take place. The coefficient of an independent variable in this case measures the
predicted change in the probability of success when the independent variable
increases by one unit.

12. Consider the following regression equation: y = β 0+β1x1+…βk xk+ u

In which of the following cases, the dependent variable is binary?
a. y indicates the gross domestic product of a country
b. y indicates whether an adult is a college dropout
c. y indicates household consumption expenditure
d. y indicates the number of children in a family

Answer: b
Difficulty: Easy
Bloom’s: Application
A-Head: A Binary Dependent Variable: The Linear Probability Model
BUSPROG: Analytic
Feedback: The dependent variable, y is binary if it is used to indicate a qualitative
outcome.

13. Which of the following Gauss-Markov assumptions is violated by the linear

probability model?
a. The assumption of constant variance of the error term.
b. The assumption of zero conditional mean of the error term.
c. The assumption of no exact linear relationship among independent variables.
d. The assumption that none of the independent variables are constants.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: A Binary Dependent Variable: The Linear Probability Model
BUSPROG:
Feedback: The linear probability model violates the assumption of constant variance
of the error term.

14. Which of the following problems can arise in policy analysis and program
evaluation using a multiple linear regression model?
a. There exists homoscedasticity in the model.
b. The model can produce predicted probabilities that are less than zero and greater
than one.
c. The model leads to the omitted variable bias as only two independent factors can
be included in the model.
d. The model leads to an overestimation of the effect of independent variables on
the dependent variable.

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Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: More on Policy Analysis and Program Evaluation
BUSPROG:
Feedback: The model can produce predicted probabilities that are less than zero and
greater than one.

15. Consider the following regression equation: y = β 0+β1x1+…βk xk+ u

In which of the following cases, is ‘y’ a discrete variable?
a. y indicates the gross domestic product of a country
b. y indicates the total volume of rainfall during a year
c. y indicates household consumption expenditure
d. y indicates the number of children in a family

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Interpreting Regression Results with Discrete Dependent Variables
BUSPROG:
Feedback: The number of children in a family can only take a small set of integer
values. Therefore, y is a discrete variable if it measures the number of children in a
family.

16. A binary variable is a variable whose value changes with a change in the
number of observations.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Describing Qualitative Information
BUSPROG:
Feedback: A binary variable is one whose value depends on the event taking place.

17. A dummy variable trap arises when a single dummy variable describes a given
number of groups.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: A Single Dummy Independent Variable
BUSPROG:

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Feedback: A dummy variable trap arises when too many dummy variables describe
a given number of groups.

18. The dummy variable coefficient for a particular group represents the estimated
difference in intercepts between that group and the base group.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Using Dummy Variables for Multiple Categories
BUSPROG:
Feedback: The dummy variable coefficient for a particular group represents the
estimated difference in intercepts between that group and the base group.

19. The multiple linear regression model with a binary dependent variable is called
the linear probability model.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: A Binary Dependent Variable: The Linear Probability Model
BUSPROG:
Feedback: The multiple linear regression model with a binary dependent variable is
called the linear probability model.

20. A problem that often arises in policy and program evaluation is that individuals
(or firms or cities) choose whether or not to participate in certain behaviors or
programs.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: More on Policy Analysis and Program Evaluation
BUSPROG:
Feedback: A problem that often arises in policy and program evaluation is that
individuals (or firms or cities) choose whether or not to participate in certain
behaviors or programs and their choice depends on several other factors. It is not
possible to control for these factors while examining the effect of the programs.

Chapter 8

1. Which of the following is true of heteroskedasticity?

a. Heteroskedasticty causes inconsistency in the Ordinary Least Squares estimators.
b. Population R2 is affected by the presence of heteroskedasticty.

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c. The Ordinary Least Square estimators are not the best linear unbiased estimators
if heteroskedasticity is present.
d. It is not possible to obtain F statistics that are robust to heteroskedasticity of an
unknown form.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Consequences of Heteroskedasticity for OLS
BUSPROG:
Feedback: The Ordinary Least Square estimators are no longer the best linear
unbiased estimators if heteroskedasticity is present in a regression model.

2. Consider the following regression model: y i=β0+β1 xi+ui. If the first four Gauss-
Markov assumptions hold true, and the error term contains heteroskedasticity, then
_____.
a. Var(ui|xi) =0
b. Var(ui|xi) =1
c. Var(ui|xi) = σi2
d. Var(ui|xi) =σ

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Heteroskedasticity-Robust Inference after OLS Estimation
BUSPROG:
Feedback: If the first four Gauss-Markov assumptions hold and the error term
contains heteroskedasticity, then Var(ui|xi) = σi2.

3. The general form of the t statistic is _____.

estimate−hypothesized value
a.
t=
standard error

hypothesized value−estimate
b.
t=
standard error

standard error
c.
t=
estimate−hypothesized value

d. t=estimate−hypothesized value

Answer: a
Difficulty: Easy
Bloom’s: Knowledge

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A-Head: Heteroskedasticity-Robust Inference after OLS Estimation

BUSPROG:
estimate−hypothesized value
Feedback: The general form of the t statistic is
t= .
standard error

4. Which of the following is true of the OLS t statistics?

a. The heteroskedasticity-robust t statistics are justified only if the sample size is
large.
b. The heteroskedasticty-robust t statistics are justified only if the sample size is
small.
c. The usual t statistics do not have exact t distributions if the sample size is large.
d. In the presence of homoscedasticity, the usual t statistics do not have exact t
distributions if the sample size is small.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Heteroskedasticity-Robust Inference after OLS Estimation
BUSPROG:
Feedback: The heteroskedasticity-robust t statistics are justified only if the sample
size is large.

5. The heteroskedasticity-robust _____ is also called the heteroskedastcity-robust

Wald statistic.
a. t statistic
b. F statistic
c. LM statistic
d. z statistic

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Heteroskedasticity-Robust Inference after OLS Estimation
BUSPROG:
Feedback: The heteroskedasticity-robust F statistic is also called the
heteroskedastcity-robust Wald statistic.

6. Which of the following tests helps in the detection of heteroskedasticity?

a. The Breusch-Pagan test
b. The Breusch-Godfrey test
c. The Durbin-Watson test
d. The Chow test

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Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing for Heteroskedasticity
BUSPROG:
Feedback: The Breusch-Pagan test is used for the detection of heteroskedasticity in
a regression model.

7. What will you conclude about a regression model if the Breusch-Pagan test results
in a small p-value?
a. The model contains homoskedasticty.
b. The model contains heteroskedasticty.
c. The model contains dummy variables.
d. The model omits some important explanatory factors.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing for Heteroskedasticity
BUSPROG:
Feedback: The Breusch-Pagan test results in a small p-value if the regression model
contains heteroskedasticty.

8. A test for heteroskedasticty can be significant if _____.

a. the Breusch-Pagan test results in a large p-value
b. the White test results in a large p-value
c. the functional form of the regression model is misspecified
d. the regression model includes too many independent variables

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing for Heteroskedasticity
BUSPROG:
Feedback: A test for heteroskedasticty can be significant if the functional form of the
regression model is misspecified.

9. Which of the following is a difference between the White test and the Breusch-
Pagan test?
a. The White test is used for detecting heteroskedasticty in a linear regression
model while the Breusch-Pagan test is used for detecting autocorrelation.
b. The White test is used for detecting autocorrelation in a linear regression model
while the Breusch-Pagan test is used for detecting heteroskedasticity. .

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c. The number of regressors used in the White test is larger than the number of
regressors used in the Breusch-Pagan test.
d. The number of regressors used in the Breusch-Pagan test is larger than the
number of regressors used in the White test.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing for Heteroskedasticity
BUSPROG:
Feedback: The White test includes the squares and cross products of all
independent variables. Therefore, the number of regressors is larger for the White
test.

10. Which of the following is true of the White test?

a. The White test is used to detect the presence of multicollinearity in a linear
regression model.
b. The White test cannot detect forms of heteroskedasticity that invalidate the usual
Ordinary Least Squares standard errors.
c. The White test can detect the presence of heteroskedasticty in a linear regression
model even if the functional form is misspecified.
d. The White test assumes that the square of the error term in a regression model is
uncorrelated with all the independent variables, their squares and cross products.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing for Heteroskedasticity
BUSPROG:
Feedback: The White test assumes that the square of the error term in a regression
model is uncorrelated with all the independent variables, the squares of
independent variables and all the cross products.

11. Which of the following is true?

a. In ordinary least squares estimation, each observation is given a different weight.
b. In weighted least squares estimation, each observation is given an identical
weight.
c. In weighted least squares estimation, less weight is given to observations with a
higher error variance.
d. In ordinary least squares estimation, less weight is given to observations with a
lower error variance.

Answer: c
Difficulty: Easy

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Bloom’s: Knowledge
A-Head: Weighted Least Squares Estimation
BUSPROG:
Feedback: In weighted Least Squares estimation, less weight is given to
observations with a higher error variance.

12. Weighted least squares estimation is used only when _____.

a. the dependent variable in a regression model is binary
b. the independent variables in a regression model are correlated
c. the error term in a regression model has a constant variance
d. the functional form of the error variances is known

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Weighted Least Squares Estimation
BUSPROG:
Feedback: Weighted Least Squares estimation is used only when the functional form
of the error variances is known.

13. Consider the following regression equation:

y=β 0 +β 1 x 1 +u . Which of the

following indicates a functional form misspecification in E(y|x)?

a. Ordinary Least Squares estimates equal Weighted Least Squares estimates.
b. Ordinary Least Squares estimates exceed Weighted Least Squares estimates by a
small magnitude.
c. Weighted Least Squares estimates exceed Ordinary Least Squares estimates by a
small magnitude.
d. Ordinary Least Square estimates are positive while Weighted Least Squares
estimates are negative.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Weighted Least Squares Estimation
BUSPROG:
Feedback: If Ordinary Least Square estimates are positive while Weighted Least
Squares estimates are negative, the functional form of a regression equation is said
to be misspecified.

14. Which of the following tests is used to compare the Ordinary Least Squares
(OLS) estimates and the Weighted Least Squares (WLS) estimates?
a. The White test
b. The Hausman test

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c. The Durbin-Watson test

d. The Breusch-Godfrey test

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Weighted Least Squares Estimation
BUSPROG:
Feedback: The Hausman test can be used to formally compare the OLS and WLS
estimates to see if they differ by more than sampling error suggests they should.

15. The linear probability model contains heteroskedasticity unless _____.

a. the intercept parameter is zero
b. all the slope parameters are positive
c. all the slope parameters are zero
d. the independent variables are binary

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Linear Probability Model Revisited
BUSPROG:
Feedback: The linear probability model contains heteroskedasticity unless all the
slope parameters are zero.

16. The interpretation of goodness-of-fit measures changes in the presence of

heteroskedasticity.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Consequences of Heteroskedasticity for OLS
BUSPROG:
Feedback: The interpretation of goodness-of-fit measures is unaffected by the
presence of heteroskedasticty.

17. Multicollinearity among the independent variables in a linear regression model

causes the heteroskedasticity-robust standard errors to be large.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Heteroskedasticity-Robust Inference after OLS Estimation
BUSPROG:

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Feedback: Multicollinearity among the independent variables in a linear regression

model causes the heteroskedasticity-robust standard errors to be large.
18. If the Breusch-Pagan Test for heteroskedasticity results in a large p-value, the
null hypothesis of homoskedasticty is rejected.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing for Heteroskedasticity
BUSPROG:
Feedback: If the Breusch-Pagan Test for heteroskedasticity results in a large p-value,
the null hypothesis of heteroskedasticty is rejected.

19. The generalized least square estimators for correcting heteroskedasticity are
called weighed least squares estimators.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Weighted Least Squares Estimation
BUSPROG:
Feedback: The generalized least square estimators for correcting heteroskedasticity
are called weighed least squares estimators.

20. The linear probability model always contains heteroskedasticity when the
dependent variable is a binary variable unless all of the slope parameters are zero.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Linear Probability Model Revisited
BUSPROG:
Feedback: The linear probability model always contains heteroskedasticity when the
dependent variable is a binary variable unless all of the slope parameters are zero.
Chapter 9

1. Consider the following regression model: log(y) = β 0 + β1x1 + β2x12 + β3x3 + u. This
model will suffer from functional form misspecification if _____.
a. β0 is omitted from the model
b. u is heteroskedastic
c. x12 is omitted from the model
d. x3 is a binary variable

Answer: c

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Difficulty: Easy
Bloom’s: Comprehension
A-Head: Functional Form Misspecification
BUSPROG:
Feedback: The model suffers from functional form misspecification if x 12 is omitted
from the model since it is a function of x1 which is an observed explanatory variable.

2. A regression model suffers from functional form misspecification if _____.

a. a key variable is binary.
b. the dependent variable is binary.
c. an interaction term is omitted.
d. the coefficient of a key variable is zero.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Functional Form Misspecification
BUSPROG:
Feedback: A regression model suffers from functional form misspecification if an
interaction term is omitted.

3. Which of the following is true?

a. A functional form misspecification can occur if the level of a variable is used when
the logarithm is more appropriate.
b. A functional form misspecification occurs only if a key variable is uncorrelated
with the error term. .
c. A functional form misspecification does not lead to biasedness in the ordinary
least squares estimators.
d. A functional form misspecification does not lead to inconsistency in the ordinary
least squares estimators.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Functional Form Misspecification
BUSPROG:
Feedback: A functional form misspecification can occur if the level of a variable is
used when the logarithm is more appropriate.

4. Which of the following is true of Regression Specification Error Test (RESET)?

a. It tests if the functional form of a regression model is misspecified.
b. It detects the presence of dummy variables in a regression model.
c. It helps in the detection of heteroskedasticity when the functional form of the
model is correctly specified.

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d. It helps in the detection of multicollinearity among the independent variables in a

regression model.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Functional Form Misspecification
BUSPROG:
Feedback: It tests if the functional form of a regression model is misspecified.

5. A proxy variable _____.

a. increases the error variance of a regression model
b. cannot contain binary information
c. is used when data on a key independent variable is unavailable
d. is detected by running the Davidson-MacKinnon test

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Using Proxy Variables for Unobserved Explanatory Variables
BUSPROG:
Feedback: A proxy variable is used when data on a key independent variable is
unavailable.

6. Which of the following assumptions is needed for the plug-in solution to the
omitted variables problem to provide consistent estimators?
a. The error term in the regression model exhibits heteroskedasticity.
b. The error term in the regression model is uncorrelated with all the independent
variables.
c. The proxy variable is uncorrelated with the dependent variable.
d. The proxy variable has zero conditional mean.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Using Proxy Variables for Unobserved Explanatory Variables
BUSPROG:
Feedback: The error term in the regression model is uncorrelated with the proxy
variable.

7. Which of the following is a drawback of including proxy variables in a regression

model?
a. It leads to misspecification analysis.
b. It reduces the error variance.

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c. It increases the error variance.

d. It exacerbates multicollinearity.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Using Proxy Variables for Unobserved Explanatory Variables
BUSPROG:
Feedback: The inclusion of a proxy variable in a regression model exacerbates
multicollinearity.

8. Consider the following equation for household consumption expenditure:

Consmptn= β0+ β1Inc + β2Consmptn-1+ u
where ‘Consmptn’ measures the monthly consumption expenditure of a household,
‘Inc’ measures household income and ‘Consmptn -1’ is the consumption expenditure
in the previous month. Consmptn-1 is a _____ variable.
a. exogenous
b. binary variable
c. lagged dependent
d. proxy variable

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Using Proxy Variables for Unobserved Explanatory Variables
BUSPROG:
Feedback: ‘Consmptn-1’ is a lagged dependent variable in this model.

9. A measurement error occurs in a regression model when _____.

a. the observed value of a variable used in the model differs from its actual value
b. the dependent variable is binary
c. the partial effect of an independent variable depends on unobserved factors
d. the model includes more than two independent variables

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Properties of OLS under Measurement Error
BUSPROG:
Feedback: A measurement error occurs in a regression model when the observed
value of a variable used in the model differs from its actual value.

10. The classical errors-in-variables (CEV) assumption is that _____.

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a. the error term in a regression model is correlated with all observed explanatory
variables
b. the error term in a regression model is uncorrelated with all observed explanatory
variables
c. the measurement error is correlated with the unobserved explanatory variable
d. the measurement error is uncorrelated with the unobserved explanatory variable

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Properties of OLS under Measurement Error
BUSPROG:
Feedback: The classical errors-in-variables (CEV) assumption is that the
measurement error is uncorrelated with the unobserved explanatory variable.

11. Which of the following is true of measurement error?

a. If measurement error in a dependent variable has zero mean, the ordinary least
squares estimators for the intercept are biased and inconsistent.
b. If measurement error in an independent variable is uncorrelated with the
variable, the ordinary least squares estimators are unbiased.
c. If measurement error in an independent variable is uncorrelated with other
independent variables, all estimators are biased.
d. If measurement error in a dependent variable is correlated with the independent
variables, the ordinary least squares estimators are unbiased.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Properties of OLS under Measurement Error
BUSPROG:
Feedback: If measurement error in an independent variable is uncorrelated with the
variable, the ordinary least squares estimators are unbiased.

12. Sample selection based on the dependent variable is called _____.

a. random sample selection
b. endogenous sample selection
c. exogenous sample selection
d. stratified sample selection

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Missing Data, Nonrandom Samples, and Outlying Observations
BUSPROG:

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Feedback: Sample selection based on the dependent variable is called endogenous

sample selection.

13. The method of data collection in which the population is divided into
nonoverlapping, exhaustive groups is called _____.
a. random sampling
b. stratified sampling
c. endogenous sampling
d. exogenous sampling

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Missing Data, Nonrandom Samples, and Outlying Observations
BUSPROG:
Feedback: The method of data collection in which the population is divided into
nonoverlapping, exhaustive groups is called stratified sampling.

14. Which of the following types of sampling always causes bias or inconsistency in
the ordinary least squares estimators?
a. Random sampling
b. Exogenous sampling
c. Endogenous sampling
d. Stratified sampling

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Missing Data, Nonrandom Samples, and Outlying Observations
BUSPROG:
Feedback: Endogenous sampling always causes bias in the OLS estimators. If the
sample is based on whether the dependent variable is above or below a given
value, bias always occurs in OLS in estimating the population model.

15. Which of the following is a difference between least absolute deviations (LAD)
and ordinary least squares (OLS) estimation?
a. OLS is more computationally intensive than LAD.
b. OLS is more sensitive to outlying observations than LAD.
c. OLS is justified for very large sample sizes while LAD is justified for smaller
sample sizes.
d. OLS is designed to estimate the conditional median of the dependent variable
while LAD is designed to estimate the conditional mean.

Answer: b

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Difficulty: Easy
Bloom’s: Knowledge
A-Head: Least Absolute Deviations Estimation
BUSPROG:
Feedback: OLS is more sensitive to outlying observations than LAD.

16. An explanatory variable is called exogenous if it is correlated with the error

term.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head:
BUSPROG:
Feedback: An explanatory variable is called endogenous if it is correlated with the
error term.

17. A multiple regression model suffers from functional form misspecification when
it does not properly account for the relationship between the dependent and the
observed explanatory
variables.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Functional Form Misspecification
BUSPROG:
Feedback: A multiple regression model suffers from functional form misspecification
when it does not
properly account for the relationship between the dependent and the observed
explanatory
variables.

18. The measurement error is the difference between the actual value of a variable
and its reported value.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Properties of OLS under Measurement Error
BUSPROG:
Feedback: The measurement error is the difference between the actual value of a
variable and its reported value.

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19. Studentized residuals are obtained from the original OLS residuals by dividing
them by an estimate of their standard deviation.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Missing Data, Nonrandom Samples, and Outlying Observations
BUSPROG:
Feedback: Studentized residuals are obtained from the original OLS residuals by
dividing them by an estimate of their standard deviation.

20. The Least Absolute Deviations (LAD) estimators in a linear model minimize the
sum of squared residuals.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Least Absolute Deviations Estimation
BUSPROG:
Feedback: The Least Absolute Deviations (LAD) estimators in a linear model
minimize the sum of the absolute values of the residuals.

Chapter 10

1. Which of the following correctly identifies a difference between cross-sectional

data and time series data?
a. Cross-sectional data is based on temporal ordering, whereas time series data is
not.
b. Time series data is based on temporal ordering, whereas cross-sectional data is
not.
c. Cross-sectional data consists of only qualitative variables, whereas time series
data consists of only quantitative variables.
d. Time series data consists of only qualitative variables, whereas cross-sectional
data does not include qualitative variables.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Nature of Time Series Data
BUSPROG:
Feedback: Time series data is based on temporal ordering, whereas cross sectional
data is not.

2. A stochastic process refers to a:

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a. sequence of random variables indexed by time.

b. sequence of variables that can take fixed qualitative values.
c. sequence of random variables that can take binary values only.
d. sequence of random variables estimated at the same point of time.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Nature of Time Series Data
BUSPROG:
Feedback: A stochastic process refers to a sequence of random variables indexed by
time.

3. The sample size for a time series data set is the number of:
a. variables being measured.
b. time periods over which we observe the variables of interest less the number of
variables being measured.
c. time periods over which we observe the variables of interest plus the number of
variables being measured.
d. time periods over which we observe the variables of interest.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Nature of Time Series Data
BUSPROG:
Feedback: The sample size for a time series data set is the number of time periods
over which we observe the variables of interest.

4. The model: Yt = β0 + β1ct + ut, t = 1,2,…….n, is an example of a(n):

a. autoregressive conditional heteroskedasticity model.
b. static model.
c. finite distributed lag model.
d. infinite distributed lag model.

Answer: b
Difficulty: Medium
Bloom’s: Comprehension
A-Head: Examples of Time Series Regression Models
BUSPROG:
Feedback: The model: yt = β0 + β1ct + ut, t = 1,2,…….,n, is an example of a static
model.

5. A static model is postulated when:

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a. a change in the independent variable at time ‘t’ is believed to have an effect on

the dependent variable at period ‘t + 1’.
b. a change in the independent variable at time ‘t’ is believed to have an effect on
the dependent variable for all successive time periods.
c. a change in the independent variable at time ‘t’ does not have any effect on the
dependent variable.
d. a change in the independent variable at time ‘t’ is believed to have an immediate
effect on the dependent variable.

Answer: d
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Examples of Time Series Regression Models
BUSPROG:
Feedback: A static model is postulated when a change in the independent variable
at time ‘t’ is believed to have an immediate effect on the dependent variable.

6. Refer to the following model.

yt = α0 + β0st + β1st-1 + β2st-2 + β3st-3 + ut
This is an example of a(n):
a. infinite distributed lag model.
b. finite distributed lag model of order 1.
c. finite distributed lag model of order 2.
d. finite distributed lag model of order 3.

Answer: d
Difficulty: Moderate
Bloom’s: Comprehension
A-Head: Examples of Time Series Regression Models
BUSPROG:
Feedback: The model: yt = α0 + β0st + β1st-1 + β2st-2 + β3st-3 + ut, is an example of a
finite distributed lag model of order 3.

7. Refer to the following model.

yt = α0 + β0st + β1st-1 + β2st-2 + β3st-3 + ut
β0 + β1 + β2 + β3 represents:
a. the short-run change in y given a temporary increase in s.
b. the short-run change in y given a permanent increase in s.
c. the long-run change in y given a permanent increase in s.
d. the long-run change in y given a temporary increase in s.

Answer: c
Difficulty: Moderate
Bloom’s: Comprehension

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A-Head: Examples of Time Series Regression Models

BUSPROG:
Feedback: In the model, the sum of the coefficients on current and lagged z, β 0 + β1
+ β2 + β3 represents the long-run change in y given a permanent change in s.

8. Which of the following is an assumption on which time series regression is based?

a. A time series process follows a model that is nonlinear in parameters.
b. In a time series process, no independent variable is a perfect linear combination
of the others.
c. In a time series process, at least one independent variable is a constant.
d. For each time period, the expected value of the error u t, given the explanatory
variables for all time periods, is positive.

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Finite Sample Properties of OLS under Classical Assumptions
BUSPROG:
Feedback: Time series regression is based on the assumption that no independent
variables are constant.

9. Under the assumptions of time series regression, which of the following

statements will be true of the following model: y t = α0 + α1dt + ut?
a. d can have a lagged effect on y.
b. ut can be correlated with past and future values of d.
c. Changes in the error term cannot cause future changes in d.
d. Changes in d cannot cause changes in y at the same point of time.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Finite Sample Properties of OLS under Classical Assumptions
BUSPROG:
Feedback: Under the assumptions of time series regression, changes in the error
term cannot cause future changes in d, in the given model.

10. If an explanatory variable is strictly exogenous it implies that:

a. changes in the lag of the variable does not affect future values of the dependent
variable.
b. the variable is correlated with the error term in all future time periods.
c. the variable cannot react to what has happened to the dependent variable in the
past.
d. the conditional mean of the error term given the variable is zero.

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Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Finite Sample Properties of OLS under Classical Assumptions
BUSPROG:
Feedback: If an explanatory variable is strictly exogenous it implies that the variable
cannot react to what has happened to the dependent variable in the past.

11. A study which observes whether a particular occurrence influences some

outcome is referred to as a(n):
a. event study.
b. exponential study.
c. laboratory study.
d. comparative study.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Functional Form, Dummy Variables, and Index Numbers.
BUSPROG:
Feedback: A study which observes whether a particular occurrence influences some
outcome is referred to as an event study.

12. With base year 1990, the index of industrial production for the year 1999 is 112.
What will be the value of the index in 1999, if the base year is changed to 1982 and
the index measured 96 in 1982?
a. 112.24
b. 116.66
c. 85.71
d. 92.09

Answer: b
Difficulty: Moderate
Bloom’s: Apply
A-Head: Functional Form, Dummy Variables, and Index Numbers.
BUSPROG: Analytic
Feedback: If the base year is changed to 1982, the new index of industrial
production for 1999 will equal 100(112/96) = 116.67.

13. Which of the following statements is true?

a. The average of an exponential time series is a linear function of time.
b. The average of a linear sequence is an exponential function of time.
c. When a series has the same average growth rate from period to period, it can be
approximated with an exponential trend.

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d. When a series has the same average growth rate from period to period, it can be
approximated with a linear trend.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Trends and Seasonality
BUSPROG:
Feedback: When a series has the same average growth rate from period to period, it
can be approximated with an exponential trend.

14. Adding a time trend can make an explanatory variable more significant if:
a. the dependent and independent variables have similar kinds of trends, but
movement in the independent variable about its trend line causes movement in the
dependent variable away from its trend line.
b. the dependent and independent variables have similar kinds of trends and
movement in the independent variable about its trend line causes movement in the
dependent variable towards its trend line.
c. the dependent and independent variables have different kinds of trends and
movement in the independent variable about its trend line causes movement in the
dependent variable towards its trend line.
d. the dependent and independent variables have different kinds of trends, but
movement in the independent variable about its trend line causes movement in the
dependent variable away from its trend line.

Answer: d
Difficulty: Hard
Bloom’s: Knowledge
A-Head: Trends and Seasonality
BUSPROG:
Feedback: Adding a time trend can make an explanatory variable more significant if
the dependent and independent variables have different kinds of trends and
movement in the independent variable about its trend line causes movement in the
dependent variable away from its trend line.

15. A seasonally adjusted series is one which:

a. has had seasonal factors added to it.
b. has seasonal factors removed from it.
c. has qualitative explanatory variables representing different seasons.
b. has qualitative dependent variables representing different seasons.

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge

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A-Head: Trends and Seasonality

BUSPROG:
Feedback: A seasonally adjusted series is one which has seasonal factors removed
from it.

16. Economic time series are outcomes of random variables.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Nature of Time Series Data
BUSPROG:
Feedback: Economic time series are outcomes of random variables.

17. In a static model, one or more explanatory variables affect the dependent
variable with a lag.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Examples of Time Series Regression Models
BUSPROG:
Feedback: In a finite distributed lag model, one or more explanatory variables affect
the dependent variable with a lag. In a static model, no lags are included.

18. Time series regression is based on series which exhibit serial correlation.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Finite Sample Properties of OLS under Classical Assumptions
BUSPROG:
Feedback: One of the assumptions of time series regression is that there should be
no serial correlation in the concerned series.

19. Price indexes are necessary for turning a time series measured in real value into
nominal value.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Functional Forms, Dummy Variables, and Index Numbers.
BUSPROG:

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Feedback: Price indexes are necessary for turning a time series measured in
nominal value into real value.

20. Dummy variables can be used to address the problem of seasonality in

regression models.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Trends and Seasonality
BUSPROG:
Feedback: Dummy variables can be used to account for seasonality in the
dependent variable, the independent variables, or both and thus, address the
problem of seasonality in regression models.

Chapter 11

1. A process is stationary if:

a. any collection of random variables in a sequence is taken and shifted ahead by h
time periods; the joint probability distribution changes.
b. any collection of random variables in a sequence is taken and shifted ahead by h
time periods, the joint probability distribution remains unchanged.
c. there is serial correlation between the error terms of successive time periods and
the explanatory variables and the error terms have positive covariance.
d. there is no serial correlation between the error terms of successive time periods
and the explanatory variables and the error terms have positive covariance.

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Stationary and Weakly Dependent Time Series
BUSPROG:
Feedback: A process is stationary if any collection of random variables in a
sequence is taken and shifted ahead by h time periods; the joint probability
distribution remains unchanged.

2. A stochastic process {xt: t = 1,2,….} with a finite second moment [E(x t2) < ∞] is
covariance stationary if:
a. E(xt) is variable, Var(xt) is variable, and for any t, h ≥ 1, Cov(xt, xt+h) depends only
on ‘h’ and not on ‘t’.
b. E(xt) is variable, Var(xt) is variable, and for any t, h ≥ 1, Cov(xt, xt+h) depends only
on ‘t’ and not on h.

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c. E(xt) is constant, Var(xt) is constant, and for any t, h ≥ 1, Cov(xt, xt+h) depends
only on ‘h’ and not on ‘t’.
d. E(xt) is constant, Var(xt) is constant, and for any t, h ≥ 1, Cov(xt, xt+h) depends
only on ‘t’ and not on ‘h’.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Stationary and Weakly Dependent Time Series
BUSPROG:
Feedback: A stochastic process {xt: t = 1,2,….} with a finite second moment [E(x t2)
< ∞] is covariance stationary if E(xt) is constant, Var(xt) is constant, and for any t, h
≥ 1, Cov(xt, xt+h) depends only on ‘h’ and not on ‘t’.

3. A covariance stationary time series is weakly dependent if:

a. the correlation between the independent variable at time ‘t’ and the dependent
variable at time ‘t + h’ goes to ∞ as h → 0.
b. the correlation between the independent variable at time ‘t’ and the dependent
variable at time ‘t + h’ goes to 0 as h → ∞.
c. the correlation between the independent variable at time ‘t’ and the independent
variable at time ‘t + h’ goes to ∞ as h → 0.
d. the correlation between the independent variable at time ‘t’ and the independent
variable at time ‘t + h’ goes to 0 as h → ∞.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Stationary and Weakly Dependent Time Series
BUSPROG:
Feedback: A covariance stationary time series is weakly dependent if the correlation
between the independent variable at time ‘t’ and the independent variable at time
‘t + h’ goes to 0 as h → ∞.

4. The model yt = et + β1et – 1 + β2et – 2 , t = 1, 2, ….. , where et is an i.i.d. sequence

with zero mean and variance σ2e represents a(n):
a. static model.
b. moving average process of order one.
c. moving average process of order two.
d. autoregressive process of order two.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Stationary and Weakly Dependent Time Series

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BUSPROG:
Feedback: The model yt = et + β1et – 1 + β2et – 2 , t = 1, 2, ….. , where et is an i.i.d.
sequence with zero mean and variance σ2e, represents an moving average process
of order two.

5. The model xt = α1xt – 1 + et , t =1,2,…. , where et is an i.i.d. sequence with zero

mean and variance σ2e represents a(n):
a. moving average process of order one.
b. moving average process of order two.
c. autoregressive process of order one.
d. autoregressive process of order two.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Stationary and Weakly Dependent Time Series
BUSPROG:
Feedback: The model xt = α1xt – 1 + et , t =1,2,…. , where et is an i.i.d. sequence with
zero mean and variance σ2e, represents an autoregressive process of order one.

6. Which of the following is assumed in time series regression?

a. There is no perfect collinearity between the explanatory variables.
b. The explanatory variables are contemporaneously endogenous.
c. The error terms are contemporaneously heteroskedastic.
d. The explanatory variables cannot have temporal ordering.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Asymptotic Properties of OLS
BUSPROG:
Feedback: One of the assumptions of time series regression is that there should be
no perfect collinearity between the explanatory variables.

7. Suppose ut is the error term for time period ‘t’ in a time series regression model
the explanatory variables are xt = (xt1, xt2 …., xtk). The assumption that the errors
are contemporaneously homoskedastic implies that:
a. Var(ut|xt) = √σ.
b. Var(ut|xt) = ∞.
c. Var(ut|xt) = σ2.
d. Var(ut|xt) = σ.

Answer: c
Difficulty: Moderate

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Bloom’s: Knowledge
A-Head: Asymptotic Properties of OLS
BUSPROG:
Feedback: If ut is the error term for time period ‘t’ and xt is a matrix consisting of all
independent variables for time ‘t’, the assumption of contemporaneously
homoskedasticity implies that Var(ut|xt) = σ2.

8. Which of the following statements is true?

a. A model with a lagged dependent variable cannot satisfy the strict exogeneity
assumption.
b. Stationarity is critical for OLS to have its standard asymptotic properties.
c. Efficient static models can be estimated for nonstationary time series.
d. In an autoregressive model, the dependent variable in the current time period
varies with the error term of previous time periods.

Answer: a
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Asymptotic Properties of OLS
BUSPROG:
Feedback: A model with a lagged dependent variable cannot satisfy the strict
exogeneity assumption. When explanatory variables are correlated with the past,
strict exogeneity does not hold.

9. Consider the model: yt = α0 + α1rt1 + α2rt2 + ut. Under weak dependence, the
condition sufficient for consistency of OLS is:
a. E(rt1|rt2) = 0.
b. E(yt |rt1, rt2) = 0.
c. E(ut |rt1, rt2) = 0.
d. E(ut |rt1, rt2) = ∞.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Asymptotic Properties of OLS
BUSPROG:
Feedback: If a time series model is weakly dependent, the condition sufficient for
consistency of OLS is E(ut|rt1, rt2) = 0.

10. The model yt = yt – 1 + et, t = 1, 2, … represents a:

a. AR(2) process.
b. MA(1) process.
c. random walk process.
d. random walk with a drift process.

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Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Using Highly Persistent Time Series in Regression Analysis
BUSPROG:
Feedback: The model yt = yt – 1 + et, t = 1, 2, … represents a random walk process.

11. Which of the following statements is true?

a. A random walk process is stationary.
b. The variance of a random walk process increases as a linear function of time.
c. Adding a drift term to a random walk process makes it stationary.
d. The variance of a random walk process with a drift decreases as an exponential
function of time.

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Using Highly Persistent Time Series in Regression Analysis
BUSPROG:
Feedback: The variance of a random walk process increases as a linear function of
time. This is because the variance of the dependent variable is equal to σ 2t.

12. If a process is said to be integrated of order one, or I(1), _____.

a. it is stationary at level
b. averages of such processes already satisfy the standard limit theorems
c. the first difference of the process is weakly dependent
d. it does not have a unit root

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Using Highly Persistent Time Series in Regression Analysis
BUSPROG:
Feedback: If a process is said to be integrated of order one, or I(1), the first
difference of the process is weakly dependent.

13. Which of the following statements is true of dynamically complete models?

a. There is scope of adding more lags to the model to better forecast the dependent
variable.
b. The problem of serial correlation does not exist in dynamically complete models.
c. All econometric models are dynamically complete.
d. Sequential endogeneity is implied by dynamic completeness..

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Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Dynamically Complete Models and the Absence of Serial Correlation
BUSPROG:
Feedback: The problem of serial correlation does not exist in dynamically complete
models.

14. In the model yt = α0 + α1xt1 + α2xt2 + ….. + αkxtk + ut, the explanatory variables,
xt = (xt1, xt2 …., xtk), are sequentially exogenous if:
a. E(ut|xt , xt-1, ……) = E(ut) = 0, t = 1,2, ….
b. E(ut|xt , xt-1, ……) ≠ E(ut) = 0, t = 1,2, ….
c. E(ut|xt , xt-1, ……) = E(ut) > 0, t = 1,2, ….
d. E(ut|xt , xt-1, ……) = E(ut) = 1, t = 1,2, ….

Answer: a
Difficulty: Moderate
Bloom’s: knowledge
A-Head: Dynamically Complete Models and the Absence of Serial Correlation
BUSPROG:
Feedback: In the given model, the explanatory variables are sequentially exogenous
if E(ut|xt , xt-1, ……) = E(ut) = 0, t = 1,2, ….

15. If ut refers to the error term at time ‘t’ and yt – 1 refers to the dependent variable
at time ‘t – 1’, for an AR(1) process to be homoskedastic, it is required that:
a. Var(ut|yt – 1) > Var(yt|yt-1) = σ2.
b. Var(ut|yt – 1) = Var(yt|yt-1) > σ2.
c. Var(ut|yt – 1) < Var(yt|yt-1) = σ2.
d. Var(ut|yt – 1) = Var(yt|yt-1) = σ2.

Answer: d
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: The Homoskedasticity Assumption for Time Series Models
BUSPROG:
Feedback: If ut refers to the error term at time ‘t’ and yt – 1 refers to the dependent
variable at time ‘t – 1’, for an AR(1) model to be homoskedastic, it is required that
Var(ut|yt – 1) = Var(yt|yt-1) = σ2.

16. Covariance stationarity focusses only on the first two moments of a stochastic
process.

Answer: True
Difficulty: Easy

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Bloom’s: Knowledge
A-Head: Stationary and Weakly Dependent Time Series
BUSPROG:
Feedback: Covariance stationarity focusses only on the first two moments of a
stochastic process: the mean and variance, which are constant over time.

17. Under adaptive expectations, the expected current value of a variable does not
depend on a recently observed value of the variable.

Answer: False
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Asymptotic Properties of OLS
BUSPROG:
Feedback: Under adaptive expectations, the expected current value of a variable
adapts to a recently observed value of the variable.

18. Weakly dependent processes are said to be integrated of order zero.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Stationary and Weakly Dependent Time Series
BUSPROG:
Feedback: Weakly dependent processes are said to be integrated of order zero, or
I(0).

19. Sequential exogeneity is implied by dynamic completeness.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Dynamically Complete Models and the Absence of Serial Correlation
BUSPROG:
Feedback: Sequential exogeneity is implied by dynamic completeness.

20. The homoskedasticity assumption in time series regression suggests that the
variance of the error term cannot be a function of time.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Homoskedasticity Assumption for Time Series Models
BUSPROG:

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Feedback: The homoskedasticity assumption in time series regression suggests that

the variance of the error term cannot be a function of time. Homeskedasticity
implies that the variance of the error term is constant and hence cannot be a
function of time.
Chapter 12

1. In the presence of serial correlation:

a. estimated standard errors remain valid.
b. estimated test statistics remain valid.
c. estimated OLS values are not BLUE.
d. estimated variance does not differ from the case of no serial correlation.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Properties of OLS with Serially Correlated Errors
BUSPROG:
Feedback: As the Gauss-Markov Theorem requires both homoscedasticity and
serially uncorrelated errors, OLS in no longer BLUE in the presence of serial
correlation.

2. When a series is stationary, weakly dependent, and has serial correlation:

a. the adjusted R2 is inconsistent, while R2 is a consistent estimator of the
population parameter.
b. the adjusted R2 is consistent, while R2 is an inconsistent estimator of the
population parameter.
c. both the adjusted R2 and R2 are inconsistent estimators of the population
parameter.
d. both the adjusted R2 and R2 are consistent estimators of the population
parameter.

Answer: d
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Properties of OLS with Serially Correlated Errors
BUSPROG:
Feedback: When a series is stationary, weakly dependent, and has serial correlation
both the adjusted R2 and R2 are consistent estimators of the population parameter
as the calculation of R2 and adjusted R2 is based on the variance of the dependent
variable and the error term, which do not change over time.

3. Which of the following is a test for serial correlation in the error terms?
a. Johansen test
b. Dickey Fuller test

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c. Durbin Watson test

d. White test

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing for Serial Correlation
BUSPROG:
Feedback: The Durbin Watson test can be used to test for serial correlation in error
terms.

4. For a given significance level, if the calculated value of the Durbin Watson
statistic lies between the lower critical value and the upper critical value, _____.
a. the hypothesis of no serial correlation is accepted
b. the hypothesis of no serial correlation is rejected
c. the test is inconclusive
d. the hypothesis of heteroskedasticity is accepted

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Testing for Serial Correlation
BUSPROG:
Feedback: For a given significance level, if the calculated value of the Durbin
Watson statistic lies between the lower critical value and upper critical value, the
test is inconclusive.

5. Which of the following statements is true?

a. When explanatory variables are not strictly exogenous, the t test for serial
correlation is valid.
b. When explanatory variables are not strictly exogenous, the Durbin Watson test
for serial correlation is valid.
c. Breusch-Godfrey test can be used to check for second order serial correlation.
d. White test can be used to check for second order serial correlation.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Testing for Serial Correlation
BUSPROG:
Feedback: Breusch-Godfrey test can be used to check for second order serial
correlation.

6. The Breusch-Godfrey test statistic follows a:

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a. χ2distribution.
b. t distribution.
c. normal distribution.
d. F distribution.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing for Serial Correlation
BUSPROG:
Feedback: The Breusch-Godfrey test statistic follows a χ2distribution.

7. In a model based on a weakly dependent time series with serial correlation and
strictly exogenous explanatory variables, _____.
a. the feasible generalized least square estimates are unbiased
b. the feasible generalized least square estimates are BLUE
c. the feasible generalized least square estimates are asymptotically more efficient
than OLS estimates
d. the feasible generalized least square estimates are asymptotically less efficient
than OLS estimates

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Correcting for Serial Correlation with Strictly Exogenous Regressors
BUSPROG:
Feedback: In a model based on a weakly dependent time series with serial
correlation and strictly exogenous explanatory variables the feasible generalized
least square estimates are asymptotically more efficient than OLS estimates.

8. Which of the following is an example of FGLS estimation?

a. Dickey-Fuller estimation
b. Vector error correction estimation
c. Prais-Winsten estimation
d. OLS estimation.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Correcting for Serial Correlation with Strictly Exogenous Regressors
BUSPROG:
Feedback: Prais-Winsten estimation is a type of FGLS estimation.

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9. Which of the following is the reason why standard errors measured by OLS differ
from standard errors measured through Prais-Winsten transformation?
a. OLS standard errors account for serial correlation, whereas Prais-Winsten
estimations do not.
b. Prais-Winsten standard errors account for serial correlation, whereas OLS
estimations do not.
c. Prais-Winsten standard errors account for heteroskedasticity, whereas OLS
estimations do not.
d. OLS standard errors account for heteroskedasticity, whereas Prais-Winsten
estimations do not.

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Correcting for Serial Correlation with Strictly Exogenous Regressors
BUSPROG:
Feedback: The standard errors measured by OLS differ from the standard errors
measured by Prais-Winsten transformation because Prais-Winsten standard errors
account for serial correlation, whereas OLS estimations do not.

10. Which of the following identifies an advantage of first differencing a time-series?

a. First differencing eliminates most of the serial correlation.
b. First differencing eliminates most of the heteroskedastcicty.
c. First differencing eliminates most of the multicollinearity.
d. First differencing eliminates the possibility of spurious regression.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Differencing and Serial Correlation
BUSPROG:
Feedback: First differencing of a time-series helps eliminate most of the serial
correlation.

11. Which of the following is a limitation of serial correlation-robust standard errors?

a. The serial correlation-robust standard errors are smaller than OLS standard errors
when there is serial correlation.
b. The serial correlation-robust standard errors can be poorly behaved when there is
substantial serial correlation and the sample size is small.
c. The serial correlation-robust standard errors cannot be calculated for
autoregressive processes of an order greater than one.
d. The serial correlation-robust standard errors cannot be calculated after relaxing
the assumption of homoskedasticity.

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Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Serial Correlation-Robust Inference after OLS
BUSPROG:
Feedback: The serial correlation-robust standard errors can be poorly behaved when
there is substantial serial correlation and the sample size is small.

12. Which of the following statements is true?

a. Prais-Winsten and Cochrane-Orcutt transformations are consistent when
explanatory variables are not strictly exogenous.
b. The SC-robust standard errors cannot be estimated in models with lagged
dependent variables.
c. The SC-robust standard errors work better after quasi-differencing a time series
that is expected to be serially correlated.
d. Estimation of SC-robust standard errors is independent of the sample size.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Serial Correlation-Robust Inference after OLS
BUSPROG:
Feedback: The SC-robust standard errors work better after quasi-differencing a time
series that is expected to be serially correlated. Quasi-differencing helps limit serial
correlation.

13. In the presence of heteroskedasticity, the usual OLS estimates of:

a. standard errors are valid, whereas the t statistics and F statistics are invalid.
b. t statistics are valid, but the standard errors and F statistics are invalid.
c. F statistics are valid, but the standard errors and t statistics are invalid.
d. standard errors, t statistics, and F statistics are invalid.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Heteroskedasticity in Time Series Regression
BUSPROG:
Feedback: In the presence of heteroskedasticity, the usual OLS estimates of
standard errors, t statistics, and F statistics are invalid.

14. Which of the following tests can be used to test for heteroskedasticity in a time
series?
a. Johansen test
b. Dickey-Fuller test

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c. Breusch-Pagan test
d. Durbin’s alternative test

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Heteroskedasticity in Time Series Regression
BUSPROG:
Feedback: The Breusch-Pagan test can be used to test for heteroskedasticicty in a
time series.

15. The equation u2t = α0 + α1u2t –1 + vt is an autoregressive model in _____.

a. ut
b. u2t
c. vt
d. ut – 1

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Heteroskedasticity in Time Series Regression
BUSPROG:
Feedback: The model u2t = α0 + α1u2t – 1 + vt is an autoregressive model in u2t.

16. In presence of serial correlation, the OLS variance formula accurately estimates
the true variance of the OLS estimator.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Properties of OLS with Serially Correlated Errors
BUSPROG:
Feedback: In presence of serial correlation, the OLS variance formula either
understates or overstates the true variance of the OLS estimator.

17. Durbin’s alternative test is valid even if the explanatory variables are strictly
exogenous.

Answer: True
Difficulty: easy
Bloom’s: Knowledge
A-Head: Testing for Serial Correlation
BUSPROG:
Feedback: Durbin’s alternative test is valid even if the explanatory variables are
strictly exogenous.

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18. Consistency of feasible generalized least square estimators requires the error
term to be correlated with lags of the explanatory variable.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing for Serial Correlation
BUSPROG:
Feedback: Consistency of feasible generalized least square estimators requires the
error term to be uncorrelated with lags of the explanatory variable. Correlation will
lead to inconsistent estimates.

19. FGLS estimates are efficient when explanatory variables are not strictly
exogenous.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing for Serial Correlation
BUSPROG:
Feedback: FGLS estimates are inefficient when explanatory variables are not strictly
exogenous.

20. In time series regressions, it is advisable to check for serial correlation first,
before checking for heteroskedasticity.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Heteroskedasticity in Time Series Regression
BUSPROG:
Feedback: Serial correlation invalidates heteroskedasticity tests. Hence, it is
advisable to check for serial correlation first, before checking for heteroskedasticity
in time series regressions.
Chapter 13

1. Which of the following is a reason for using independently pooled cross sections?
a. To obtain data on different cross sectional units
b. To increase the sample size
c. To select a sample based on the dependent variable
d. To select a sample based on the independent variable

Answer: b

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Difficulty: Easy
Bloom’s: Knowledge
A-Head: Pooling Independent Cross Sections across Time
BUSPROG:
Feedback: One reason for using independently pooled cross sections is to increase
the sample size. By pooling random samples drawn from the same population, but
at different points in time, we can get more precise estimators and test statistics
with more power.

2. Pooling independent cross sections across time is useful in providing precise

estimators if _____.
a. the relationship between the dependent variable and at least some of the
independent variables remains constant over time
b. the relationship between the dependent variable and at least some of the
independent variables is linear
c. the relationship between the dependent variable and at least one of the
independent variables changes over time
d. the relationship between the dependent variable and at least one of the
independent variables is positive

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Pooling Independent Cross Sections across Time
BUSPROG:
Feedback: Pooling independent cross sections across time is useful in providing
precise estimators if the relationship between the dependent variable and at least
some of the independent variables remains constant over time.

3. A Chow test _____.

a. is used to test the presence of heteroskedasticty in a regression model.
b. is used to determine how multiple regression differs across two groups.
c. cannot detect changes in the slope coefficients of dependent variables over time.
d. cannot be computed for more than two time periods.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Pooling Independent Cross Sections across Time
BUSPROG:
Feedback: A Chow test is used to determine how multiple regression differs across
two groups.

4. Which of the following is true of a natural experiment?

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a. A natural experiment occurs when some endogenous event changes the

environment in which individuals, families, firms, or cities operate.
b. The control group in a natural experiment is randomly chosen.
c. The treatment group in a natural experiment is randomly chosen.
d. Control and treatment groups in a natural experiment arise due to an exogenous
event.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Policy Analysis with Pooled Cross Sections
BUSPROG:
Feedback: Control and treatment groups in a natural experiment arise due to an
exogenous event.

5. The average treatment effect measures _____.

a. the effect of a policy or program on the dependent variable
b. the effect of unobserved factors on observed explanatory variables
c. the effect of a change in an explanatory variable on other explanatory variables
d. the effect of unobserved factors on the average outcome of the dependent
variable

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Policy Analysis with Pooled Cross Sections
BUSPROG:
Feedback: The average treatment effect measures the effect of a policy or program
on the dependent variable.

6. Idiosyncratic error is the error that occurs due to _____.

a. incorrect measurement of an economic variable
b. unobserved factors that affect the dependent variable and change over time
c. unobserved factors that affect the dependent variable and do not change over
time
d. correlation between the independent variables

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Two-Period Panel Data Analysis
BUSPROG:
Feedback: Idiosyncratic error is the error that occurs due to unobserved factors that
affect the dependent variable and change over time.

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7. A regression model exhibits unobserved heterogeneity if _____.

a. there are unobserved factors affecting the dependent variable that change over
time
b. there are unobserved factors affecting the dependent variable that do not change
over time
c. there are unobserved factors which are correlated with the observed independent
variables
d. there are no unobserved factors affecting the dependent variable

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Two-Period Panel Data Analysis
BUSPROG:
Feedback: A regression model exhibits unobserved heterogeneity if there are
unobserved factors affecting the dependent variable but they do not change over
time.

8. Composite error is the error that occurs due to _____.

a. the incorrect measurement of explanatory variables
b. the inclusion of too many explanatory variables in the model
c. correlation between the independent variables
d. unobserved factors affecting a dependent variable

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Two-Period Panel Data Analysis
BUSPROG:
Feedback: Composite error is the error that occurs due to all unobserved factors
affecting a dependent variable.

9. Ordinary least squares estimation is subject to heterogeneity bias if _____.

a. the regression model exhibits heteroskedasticty
b. the unobserved effect is correlated with the observed explanatory variables
c. the regression model includes a lagged dependent variable
d. the explanatory variables do not change over time

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Two-Period Panel Data Analysis
BUSPROG:

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Feedback: Ordinary least estimation is subject to heterogeneity bias if the

unobserved effect is correlated with the observed explanatory variables.

10. Which of the following assumptions is required to obtain a first-differenced

estimator in a two-period panel data analysis?
a. The explanatory variable does not change over time for any cross-sectional unit.
b. The explanatory variable changes by the same amount in each time period.
c. The variance of the error term in the regression model is not constant.
d. The idiosyncratic error at each time period is uncorrelated with the explanatory
variables in both time periods.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Two-Period Panel Data Analysis
BUSPROG:
Feedback: The assumption that the idiosyncratic error at each time period is
uncorrelated with the explanatory variables in both time periods is required to
obtain a first-differenced estimator.

11. The assumption of “strict exogeneity” in a regression model means that _____
a. the dependent variable is binary
b. all explanatory variables change over time
c. the model does not include a lagged dependent variable as a regressor
d. the model includes all relevant explanatory variables

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Differencing with More Than Two Time Periods
BUSPROG:
Feedback: The assumption of “strict exogeneity” in a regression model means that
the model does not include a lagged dependent variable as a regressor.

12. A data set is a balanced panel if it _____.

a. consists of a sample of individuals, households, firms, cities, states, countries, or
a variety of other units, taken at a given point in time.
b. consists of observations on a variable or several variables over time
c. consists of data for each cross sectional unit over the same time period
d. consists of time-demeaned data for different cross sectional units

Answer: c
Difficulty: Easy
Bloom’s: Knowledge

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A-Head: Differencing with More Than Two Time Periods

BUSPROG:
Feedback: A data set is a balanced panel if it consists of data for different cross
sectional units over the same time period.

13. Which of the following assumptions is needed for the usual standard errors to be
valid when differencing with more than two time periods?
a. The regression model exhibits heteroskedasticty.
b. The differenced idiosyncratic error or ∆ uit is uncorrelated over time.

c. The unobserved factors affecting the dependent variable are time-constant.

d. The regression model includes a lagged independent variable.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Differencing with More Than Two Time Periods
BUSPROG:
Feedback: The differenced idiosyncratic error is uncorrelated over time.

14. First-differenced estimation in a panel data analysis is subject to serious biases

if _____.
a. key explanatory variables vary significantly over time
b. the explanatory variables do not change by the same unit in each time period
c. one or more of the explanatory variables are measured incorrectly
d. the regression model exhibits homoscedasticity

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Differencing with More Than Two Time Periods
BUSPROG:
Feedback: First-differenced estimation is subject to serious biases if one or more of
the explanatory variables are measured incorrectly.

15. The general approach to obtaining fully robust standard errors and test statistics
in
the context of panel data is known as _____.
a. confounding
b. differencing
c. clustering
d. attenuating

Answer: c

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Difficulty: Easy
Bloom’s: Knowledge
A-Head: Appendix 13A
BUSPROG:
Feedback: The general approach to obtaining fully robust standard errors and test
statistics in
the context of panel data is known as clustering.

16. If a random sample is drawn at each time period, pooling the resulting random
samples gives us a panel data set.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Pooling Independent Cross Sections across Time
BUSPROG:
Feedback: If a random sample is drawn at each time period, pooling the resulting
random samples gives us an independently pooled cross section.

17. A natural experiment occurs when an endogenous event changes the

environment in which individuals, families, firms, or cities operate.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Pooling Independent Cross Sections across Time
BUSPROG:
Feedback: A natural experiment occurs when an exogenous event changes the
environment in which individuals, families, firms, or cities operate.

18. One way of organizing two periods of panel data is to have only one record per
cross-sectional unit.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Two-Period Panel Data Analysis
BUSPROG:
Feedback: One way of organizing two periods of panel data is to have only one
record per cross-sectional unit.

19. Two-period panel data is used for program evaluation and policy analysis.

Answer: True

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Difficulty: Easy
Bloom’s: Knowledge
A-Head: Policy Analysis with Two-Period Panel Data
BUSPROG:
Feedback: Two-period panel data is used for program evaluation and policy analysis.
In the simplest program evaluation setup, a sample of individuals, firms, cities, and
so on is obtained in the first time period. Some of these units, those in the
treatment group, then take part in a particular program in a later time period; the
ones that do not are the control group.

20. First-differenced estimation gives unbiased estimators if the regression model

includes a lagged dependent variable.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Differencing with More Than Two Time Periods
BUSPROG:
Feedback: First-differenced estimation is subject to serious biases if the regression
model includes a lagged dependent variable.
Chapter 14

1. Which of the following assumptions is required for obtaining unbiased fixed effect
estimators?
a. The errors are heteroskedastic.
b. The errors are serially correlated.
c. The explanatory variables are strictly exogenous.
d. The unobserved effect is correlated with the explanatory variables.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Fixed Effects Estimation
BUSPROG:
Feedback: Under a strict exogeneity assumption on the explanatory variables, the
fixed effects estimator is unbiased.

2. A pooled OLS estimator that is based on the time-demeaned variables is called

the _____.
a. random effects estimator
b. fixed effects estimator
c. least absolute deviations estimator
d. instrumental variable estimator

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Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Fixed Effects Estimation
BUSPROG:
Feedback: A pooled OLS estimator that is based on the time-demeaned variables is
called the fixed effects estimator.

3. What should be the degrees of freedom (df) for fixed effects estimation if the
data set includes ‘N’ cross sectional units over ‘T’ time periods and the regression
model has ‘k’ independent variables?
a. N-kT
b. NT-k
c. NT-N-k
d. N-T-k

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Fixed Effects Estimation
BUSPROG:
Feedback: If the data set includes N cross sectional units over T time periods, the
total number of observations is NT. Since the regression model includes k
independent variables, the model should have NT-k degrees of freedom. However,
for each cross-sectional observation, we lose one df because of the time-
demeaning. Therefore, the appropriate degrees of freedom is NT - N – k.

4. Which of the following types of variables cannot be included in a fixed effects

model?
a. Dummy variable
b. Discrete dependent variable
c. Time-varying independent variable
d. Time-constant independent variable

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Fixed Effects Estimation
BUSPROG:
Feedback: A fixed effects model cannot include a time-constant independent
variable.

5. Which of the following is a property of dummy variable regression?

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a. This method is best suited for panel data sets with many cross-sectional
observations.
b. The R-squared obtained from this method is lower than that obtained from
regression on time-demeaned data.
c. The degrees of freedom cannot be computed directly with this method.
d. The major statistics obtained from this method are identical to that obtained from
regression on time-demeaned data.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Fixed Effects Estimation
BUSPROG:
Feedback: The major statistics obtained from this method are identical to that
obtained from regression on time-demeaned data.

6. Which of the following is a difference between a fixed effects estimator and a

first-difference estimator?
a. The fixed effects estimators are always larger than the first difference estimators
in a two-period panel data analysis.
b. The fixed effects estimator is more efficient than the first-difference estimator
when the idiosyncratic errors are serially uncorrelated.
c. The first difference estimator is more sensitive to nonnormality and
heteroskedasticity.
d. The bias in the first difference estimator depends on the time period (T) of
analysis while the bias in the fixed effect does not depend on T.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Fixed Effects Estimation
BUSPROG:
Feedback: The fixed effects estimator is more efficient than the first-difference
estimator when the idiosyncratic errors are serially uncorrelated.

7. Which of the following assumptions is required for obtaining unbiased random

effect estimators?
a. The idiosyncratic errors are heteroskedastic.
b. The unobserved effect is independent of all explanatory variables in all time
periods.
c. The idiosyncratic errors are serially correlated.
d. The unobserved effect is correlated with the explanatory variables.

Answer: b

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Difficulty: Easy
Bloom’s: Knowledge
A-Head: Random Effects Models
BUSPROG:
Feedback: The unobserved effect is independent of all explanatory variables in all
time periods.

8. The estimator obtained through regression on quasi-demeaned data is called the

_____.
a. random effects estimator
b. fixed effects estimator
c. hetroskedasticity-robust OLS estimator
d. instrumental variables estimator

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Random Effects Models
BUSPROG:
Feedback: The estimator obtained through regression on quasi-demeaned data is
called the random effects estimator.

9. The random effects approach _____.

a. cannot be used if the key explanatory variable is constant over time.
b. is preferred to pooled OLS because RE is generally more efficient.
c. is suitable if the Hausman test rejects the assumption that the unobserved effect
is uncorrelated with the explanatory variables.
d. is more convincing than fixed effects for policy analysis using aggregate data.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Random Effects Models
BUSPROG:
Feedback: RE is preferred to pooled OLS because RE is generally more efficient.

10. The random effects estimate is identical to the fixed effects estimate if the
^
estimated transformation parameter , θ , in generalized least squares estimation
that eliminates serial correlation between error terms is, _____.
a. less than zero
b. equal to zero
c. equal to one
d. greater than one

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Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Random Effects Models
BUSPROG:
Feedback: The random effects estimate is identical to the fixed effects estimate if

the estimated transformation parameter, θ^ , in generalized least squares

estimation that eliminates serial correlation in error terms, is equal to one.

11. Which of the following is true of the correlated random effects approach (CRE)?
a. The CRE approach assumes that the unobserved effect is uncorrelated with the
observed explanatory variables.
b. The CRE approach cannot be used if the regression model includes a time-
constant explanatory variable.
c. The CRE approach considers that the unobserved effect is correlated with the
average level of explanatory variables.
d. The CRE estimate equals the random effects estimate.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Correlated Random Effects Approach
BUSPROG:
Feedback: The CRE approach considers that the unobserved effect is correlated with
the average level of explanatory variables.

12. Which of the following is a reason for using the correlated random effects
approach?
a. It provides unbiased and consistent estimators when the idiosyncratic errors are
serially correlated.
b. It provides unbiased and consistent estimators when the idiosyncratic errors are
heteroskedastic.
c. It provides a more efficient estimate than the fixed effects approach.
d. It provides a way to include time-constant explanatory variables in a fixed effects
analysis.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Correlated Random Effects Approach
BUSPROG:

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Feedback: It provides a way to include time-constant explanatory variables in a

fixed effects analysis.

13. In the correlated random effects approach, the regression model includes _____.
a. time averages as separate explanatory variables
b. at least one dummy variable
c. more than one endogenous explanatory variable
d. an instrumental variable

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Correlated Random Effects Approach
BUSPROG:
Feedback: In the correlated random effects approach, the regression model includes
time averages as separate explanatory variables.

14. An economist wants to study the effect of income on savings. He collected data
on 120 identical twins. Which of the following methods of estimation is the most
suitable method, if income is correlated with the unobserved family effect?
a. Random effects estimation
b. Fixed effects estimation
c. Ordinary least squares estimation
d. Weighted Least squares estimation

Answer: b
Difficulty: Easy
Bloom’s: Application
A-Head: The Correlated Random Effects Approach
BUSPROG: Analytic
Feedback: Fixed effects estimation is the most suitable method, if income is
correlated with the unobserved family effect. The key requirement for using the
random effects estimation is that income is uncorrelated with the unobserved family
effect and ordinary least squares estimation will provide unbiased estimators if
income is uncorrelated with the unobserved family effect.

15. Which of the following statements is true?

a. Fixed effects estimation is not suitable when the unobserved cluster effect is
correlated with one or more explanatory variables.
b. Fixed effects approach is not applicable if the key explanatory variables change
only at the level of the cluster.
c. The ordinary least squares standard errors are incorrect when there is cluster
effect.

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d. Random effects estimation can be applied to a cluster sample only if the

unobserved cluster effect is correlated with one or more explanatory variables.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Correlated Random Effects Approach
BUSPROG:
Feedback: The ordinary least squares standard errors are incorrect when there is
cluster effect.

16. A data set is called an unbalanced panel if it has missing years for at least some
cross-sectional units in the sample.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Fixed Effects Estimation
BUSPROG:
Feedback: A data set is called an unbalanced panel if it has missing years for at
least some cross-sectional units in the sample.

17. In a random effects model, we assume that the unobserved effect is correlated
with each explanatory variable.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Random Effects Models
BUSPROG:
Feedback: In a random effects model, we assume that the unobserved effect is
uncorrelated with each explanatory variable.

18. The value of the estimated transformation parameter in generalized least

square estimation that eliminates serial correlation in error terms indicates whether
the estimates are likely to be closer to the pooled OLS or the fixed effects estimates.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Random Effects Models
BUSPROG:
Feedback: The value of the estimated transformation parameter in generalized least
squares estimation that eliminates serial correlation in error terms indicates

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whether the estimates are likely to be closer to the pooled OLS or the fixed effects
estimates.

19. The correlated random effects approach cannot be applied to models with many
time-varying explanatory variables.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Correlated Random Effects Approach
BUSPROG:
Feedback: The correlated random effects approach can be applied to models with
many time-varying explanatory variables.

20. Pooled ordinary least squares estimation is commonly applied to cluster samples
when eliminating a cluster effect via fixed effects is infeasible or undesirable.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Applying Panel Data Methods to Other Data Structures
BUSPROG:
Feedback: Pooled ordinary least squares estimation is commonly applied to cluster
samples when eliminating a cluster effect via fixed effects is infeasible or
undesirable.

Chapter 15

1. Consider the following simple regression model: y = β 0 + β1x1 + u. In order to

obtain consistent estimators of β0 and β1, when x and u are correlated, a new
variable z is introduced into the model which satisfies the following two conditions:
Cov(z,x)≠0 and Cov (z,u)=0. The variable z is called a(n) _____ variable.
a. dummy
b. instrumental
c. lagged dependent variable
d. random

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Motivation: Omitted Variables in a Simple Regression Model
BUSPROG:

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Feedback: Variable z is called an instrumental variable since Cov(z,x)≠0 and

Cov(z,u)=0.

2. Consider the following simple regression model: y = β 0 + β1x1 + u. Suppose z is an

instrument for x. Which of the following conditions denotes instrument exogeneity?
a. Cov(z,u) > 0
b. Cov(z,x) > 0
c. Cov(z,u) = 0
d. Cov(z,x) = 0

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Motivation: Omitted Variables in a Simple Regression Model
BUSPROG:
Feedback: The condition Cov(z,u) = 0 denotes instrument exogeneity in this case.

3. Consider the following simple regression model y=β0 + β1x1 + u. Suppose z is an

instrument for x. Which of the following conditions denotes instrument relevance?
a. Cov(z,u) > 0
b. Cov(z,u) < 0
c. Cov(z,x) ≠ 0
d. Cov(z,x)=0

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Motivation: Omitted Variables in a Simple Regression Model
BUSPROG:
Feedback: The condition Cov(z,x) ≠ 0 denotes instrument relevance.

4. Consider the following simple regression model y=β0 + β1x1 + u. Suppose z is an

instrument for x.
Which of the following statements is true?
a. The condition Cov(z,u) = 0 can be tested statistically.
b. The condition Cov(z,x) ≠ 0 cannot be tested statistically.
c. The instrumental variables estimator is always biased if Cov(x,u)≠0.
d. The ordinary least squares estimator is unbiased if Cov(x,u)≠0.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Motivation: Omitted Variables in a Simple Regression Model
BUSPROG:

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Feedback: The instrumental variables estimator is always biased if Cov(x,u)≠0.

5. Consider the following simple regression model y=β0 + β1x1 + u. Suppose z is an

instrument for x. if Cov(z,u) = 0 and Cov(z,x) ≠ 0, the value of β 1 in terms of
population covariances is _____.
Cov ( z , y )
a. Cov (z , x)

Cov(z , u)
b. Cov (z , x )

c. Cov(z,u)
d. Cov(z,x)

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Motivation: Omitted Variables in a Simple Regression Model
BUSPROG:
Cov (z , y )
Feedback: The value of β1 is Cov ( z , x) .

6. The sampling variance for the instrumental variables (IV) estimator is larger than
the variance for the ordinary least square estimators (OLS) because _____.
a. R2 >1
b. R2 <0
c. R2 =1
d. R2 <1

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Motivation: Omitted Variables in a Simple Regression Model
BUSPROG:
Feedback: The variance of the OLS estimator differs from the comparable formula
for the IV estimator
in that R2 appears in the denominator of the IV variance. Because an R-squared is
always less than
one, the IV variance is always larger than the OLS variance.

7. Consider the following simple regression model y=β0 + β1x1 + u. The variable z is
a poor instrument for x if _____.
a. there is a high correlation between z and x

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b. there is a low correlation between z and x

c. there is a high correlation between z and u
d. there is a low correlation between z and u

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Motivation: Omitted Variables in a Simple Regression Model
BUSPROG:
Feedback: The variable z is a poor instrument for x if there is a low correlation
between z and x.

8. Which of the following assumptions is known as exclusion restrictions?

a. The assumption that an instrumental variable is excluded from a regression
model and is correlated with the error term
b. The assumption that an instrumental variable is excluded from a regression
model and correlated with an exogenous explanatory variable
c. The assumption that an exogenous explanatory variable is excluded from a
regression model and is uncorrelated with the error term
d. The assumption that an endogenous explanatory variable excluded from a
regression model and is uncorrelated with the error term

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Two Stage Least Squares
BUSPROG:
Feedback: The assumption that an exogenous explanatory variable is excluded from
a regression model and is uncorrelated with the error term.

9. Which of the following assumptions is required for two-stage least squares

estimation method?
a. There are perfect linear relationships among the instrumental variables.
b. There is strong correlation between each instrumental variable and the error
term.
c. The conditional variance of the error term depends on an exogenous explanatory
variable.
d. The error term has zero mean.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Two Stage Least Squares
BUSPROG:

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Feedback: The error term has zero mean.

10. Which of the following is true of two stage least squares estimators?
a. The two stage least squares estimator is equal to the instrumental variable
estimator if R2 is equal to 1.
b. The two stage least squares estimators are biased if the regression model
exhibits multicollinearity.
c. The two stage least squares estimators have lower variance than the ordinary
least squares estimators.
d. The two stage least squares estimators have large standard errors when R 2 lies
close to 0.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Two Stage Least Squares
BUSPROG:
Feedback: The two stage least squares estimators are biased if the regression
model exhibits multicollinearity.

11. The necessary condition for identification of an equation is called the _____.
a. order condition
b. rank condition
c. condition of instrumental exogeneity
d. the condition of instrumental relevance.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Two Stage Least Squares
BUSPROG:
Feedback: The necessary condition for identification of an equation is called the
order condition.

12. The order condition for identification of an equation requires that there should
be _____.
a. at least one exogenous explanatory variable in a structural equation
b. at least as many excluded exogenous explanatory variables as there are included
endogenous explanatory variables
c. at least as many dummy variables in an equation as there are exogenous
explanatory variables
d. as many lagged independent variables in an equation as there are exogenous
explanatory variables

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Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Two Stage Least Squares
BUSPROG:
Feedback: The order condition for identification of an equation requires that there
should be at least as many excluded exogenous explanatory variables as there are
included endogenous explanatory variables.

13. The procedure of comparing different instrumental variables estimates of the

same parameter is an example of testing _____.
a. overidentifying restrictions
b. endogeneity
c. heteroskedasticity
d. serial correlation

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing for Endogeneity and Testing Overidentifying Restrictions
BUSPROG:
Feedback: The procedure of comparing different instrumental variables estimates of
the same parameter is an example of testing overidentifying restrictions.

14. The test for overidentifying restrictions is valid if _____.

a. the regression model exhibits heteroskedasticity
b. the regression model exhibits homoskedasticity
c. the number of instrumental variables are less than the number of endogenous
explanatory variables
d. the number of instrumental variables are just enough for obtaining consistent
estimators.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing for Endogeneity and Testing Overidentifying Restrictions
BUSPROG:
Feedback: The test for overidentifying restrictions is valid if the regression model
exhibits homoskedasticity.

15. Which of the following assumptions is required for two stage least squares
estimation with time series data but not required for two-stage least squares
estimation with cross sectional data?
a. The conditional mean of the error term is zero.

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b. The error term has constant conditional variance.

c. The model includes at least one dummy variable.
d. The error terms are not serially correlated.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Applying 2SLS to Time Series Equations
BUSPROG:
Feedback: The additional assumption required for two stage least squares
estimation using time-series data is that there is no serial correlation.

16. If the instrumental variable estimator has an upward bias, the ordinary least
square estimator always has a downward bias.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Motivation: Omitted Variables in a Simple Regression Model
BUSPROG:
Feedback: It is possible for the directions of the asymptotic biases to be different for
IV and OLS but this situation is usually rare in practice.

17. Instrumental variables cannot be used for estimating a regression equation if

the regression model suffers from the measurement error problem.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: IV Solutions to Errors-in-Variables Problems
BUSPROG:
Feedback: The instrumental variables procedure can be used for estimation if the
regression model suffers from the measurement error problem.

18. The two stage least squares estimator is less efficient than the ordinary least
squares estimator when the explanatory variables are exogenous.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing for Endogeneity and Testing Overidentifying Restrictions
BUSPROG:
Feedback: The two stage least squares estimator is less efficient than the ordinary
least squares estimator when the explanatory variables are exogenous.

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19. Increasing the number of overidentifying restrictions can cause severe biases in
two stage least squares estimators.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing for Endogeneity and Testing Overidentifying Restrictions
BUSPROG:
Feedback: Increasing the number of overidentifying restrictions can cause severe
biases in two stage least squares estimators.

20. Two stage least squares estimation cannot be applied to a panel data set.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Applying 2SLS to Pooled Cross Sections and Panel Data
BUSPROG:
Feedback: Two stage least squares estimation can be applied to a panel data set.
Chapter 16

1. In econometrics, simultaneity arises when:

a. strictly exogenous explanatory variables determine the dependent variable
through a step-by-step process.
b. the error term is correlated with both the dependent variable and explanatory
variables.
c. one or more of the explanatory variables is jointly determined with the dependent
variable.
d. both serial correlation and heteroskedasticity are present in an hypothesized
model.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: The Nature of Simultaneous Equations Models
BUSPROG:
Feedback: In econometrics, simultaneity arises when one or more of the explanatory
variables is jointly determined with the dependent variable, typically through an
equilibrium process.

2. The following simultaneous equations describe the demand and supply for a
particular good in a competitive market.
Qi = α1Pi + β1zi1 + ui1

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Qi = α2Pi + β2zi2 + ui2

Which of the following are the endogenous variables in this model?
a. Pi, zi1, and zi2
b. Pi and Qi
c. zi1, and zi2
d. ui1 and ui2

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Nature of Simultaneous Equations Models
BUSPROG:
Feedback: Pi and Qi are the endogenous variables in the given simultaneous
equation model.

3. Which of the following correctly identifies a characteristic of structural equations?

a. A structural equation should contain equal number of dependent and
independent variables.
b. A structural equation should contain equal number of endogenous and exogenous
variables.
c. A structural equation should have a behavioral, ceteris paribus interpretation on
its own.
d. A structural equation should not contain structural errors.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Nature of Simultaneous Equations Models
BUSPROG:
Feedback: A structural equation should have a behavioral, ceteris paribus
interpretation on its own.

4. The following equations represent a simultaneous equations model:

K1 = α1K2 + β1z1 + u1
K2 = α2K1 + β2z2 + u2
The reduced form equation for K2 will express:
a. K1 as a function of K2 and the error terms.
b. K2 as a function of K1 and the error terms.
c. K2 as a function of exogenous variables and the error terms.
d. K1 as a function of exogenous variables, the error terms, and K 2.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge

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A-Head: Simultaneity Bias in OLS

BUSPROG:
Feedback: For the given simultaneous equations model, the reduced form equation
for K2 will express K2 as a function of exogenous variables and the error terms.

5. The following equations represent a simultaneous equations model:

K1 = α1K2 + β1z1 + u1
K2 = α2K1 + β2z2 + u2
OLS will suffer from simultaneity bias if:
a. u1 is correlated with z1.
b. z1 is correlated with z2.
c. K2 is correlated with u1.
d. K1 is correlated with u1.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Simultaneity Bias in OLS
BUSPROG:
Feedback: In the given simultaneous equation model, OLS will suffer from
simultaneity bias if K2 is correlated with u1.

6. Refer to the simultaneous equations model above. The reduced form error from
the reduced form equation for K2 will be a:
a. quadratic function of u1 and u2, and correlated with z1 and z2.
b. quadratic function of u1 and u2, and uncorrelated with z1 and z2.
c. linear function of u1 and u2, and correlated with z1 and z2.
d. linear function of u1 and u2, and uncorrelated with z1 and z2.

Answer: d
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Simultaneity Bias in OLS
BUSPROG:
Feedback: The reduced form error from the reduced form equation for K 2 will be a
linear function of u1 and u2, and uncorrelated with z1 and z2.

7. Exclusion restrictions are said to be imposed in a two-equation simultaneous

equations model if it is assumed that:
a. certain exogenous variables do not appear in the first equation and others are
absent from the second equation.
b. certain endogenous variables do not appear in the first equation and others are
absent from the second equation.
c. the error terms in each equation is uncorrelated with the exogenous variables.

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b. the error terms in each equation is correlated with the exogenous variables.

Answer: a
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Identifying and Estimating a Structural Equation
BUSPROG:
Feedback: Exclusion restrictions are said to be imposed in a two-equation
simultaneous equations model if it is assumed that certain exogenous variables do
not appear in the first equation and others are absent from the second equation.

8. Which of the following is a method which can be used for estimation in

simultaneous equations models?
a. Feasible generalized least squares estimation
b. Prais-Winsten transformation
c. Cochrane-Orcutt transformation
d. Two stage least squares estimation

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Identifying and Estimating a Structural Equation
BUSPROG:
Feedback: The two stage least squares estimation can be used in simultaneous
equations models.

9. The rank condition for identification of a structural equation states that the first
equation in a two-equation simultaneous equations model is identified if, and only if:
a. the first equation contains at least one exogenous variable (with a nonzero
coefficient) that is excluded from the second equation.
b. the first equation contains at least two exogenous variables (with a nonzero
coefficient) that are excluded from the second equation.
c. the second equation contains at least one exogenous variable (with a nonzero
coefficient) that is excluded from the first equation.
d. the second equation contains at least two exogenous variables (with a nonzero
coefficient) that are excluded from the first equation.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Identifying and Estimating a Structural Equation
BUSPROG:
Feedback: The rank condition for identification of a structural equation states that
the first equation in a two-equations simultaneous equations model is identified if,

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and only if, the second equation contains at least one exogenous variable (with a
nonzero coefficient) that is excluded from the first equation.

10. Which of the following statements is true?

a. The use of 2SLS is applicable to simultaneous equations model with at most two
equations.
b. Identification of simultaneous equations with three or more equations is based on
matrix algebra.
c. 2SLS method used in the estimation of simultaneous equations is an example of a
system estimation method.
d. The maximum number of equations permissible for a simultaneous equations
model is four.

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Systems with More than Two Equations
BUSPROG:
Feedback: Identification of simultaneous equations with three or more equations is
based on matrix algebra.

11. An equation in the simultaneous equations model satisfies the order condition
for identification if:
a. the number of excluded endogenous variables from the equation is at most as
large as the number of right-hand side exogenous variables.
b. the number of excluded endogenous variables from the equation is at least as
large as the number of right-hand side exogenous variables.
c. the number of excluded exogenous variables from the equation is at most as
large as the number of right-hand side endogenous variables.
d. the number of excluded exogenous variables from the equation is at least as
large as the number of right-hand side endogenous variables.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Systems with More than Two Equations
BUSPROG:
Feedback: An equation in the simultaneous equations model satisfies the order
condition for identification if the number of excluded exogenous variables from the
equation is at least as large as the number of right-hand side endogenous variables.

12. A predetermined variable in a simultaneous equations model is:

a. a constant.
b. the error term.

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c. a lagged variable.
d. an omitted variable.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Simultaneous Equations Models with Time Series
BUSPROG:
Feedback: A predetermined variable in a simultaneous equations model is a lagged
variable.

13. Which of the following correctly highlights a limitation of applying simultaneous

equation models to time series data?
a. Most time series have variables with a unit root and 2SLS is complicated when
applied to equations with such variables.
b. 2SLS estimates are inefficient when applied to variables that are not in their
levels but in first differences.
c. It is difficult to form simultaneous equations which satisfy the rank and order
conditions using time series data.
d. The problem of serial correlation greatly limits the efficiency of simultaneous
equation models and leads to inefficient estimations.

Answer: a
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Simultaneous Equations Models with Time Series
BUSPROG:
Feedback: Most time series have variables with a unit root and 2SLS is complicated
when applied to equations with such variables.

14. An alternative to using simultaneous equation models with panel data is:
a. to use OLS estimates after first differencing the data.
b. to use fixed effects transformation on the equations and then apply 2SLS.
c. to convert the equations into reduced form and then apply feasible generalized
least squares.
d. to convert the equations into reduced form and then apply OLS.

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Simultaneous Equations Model with Panel Data
BUSPROG:
Feedback: An alternative to using simultaneous equation models with panel data is
to use fixed effects transformation on the equations and then apply 2SLS.

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15. Which of the following statements is true?

a. White test can be used to efficiently determine the presence of serial correlation
in panel data.
b. The t statistic is not an efficient test to determine serial correlation in panel data.
c. Instrumental variables for both endogenous and exogenous variables are required
for estimating simultaneous equation models concerned with panel data.
d. 2SLS should be applied to simultaneous equation models with panel data only
after removing the unobserved effects from the equations of interest.

Answer: d
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Simultaneous Equations Model with Panel Data
BUSPROG:
Feedback: 2SLS should be applied to simultaneous equation models with panel data
only after removing the unobserved effects from the equations of interest.

16. A simultaneous equations model is suitable whenever two variables are

determined simultaneously.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Nature of Simultaneous Equations Models
BUSPROG:
Feedback: Just because two variables are determined simultaneously, it does not
imply that a simultaneous equations model is suitable. The criteria for using a
simultaneous equations model is that each equation in the model should make
sense in isolation from the other equation.

17. OLS is biased and inconsistent when applied to a structural equation in a

simultaneous equations system.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Simultaneity Bias in OLS
BUSPROG:
Feedback: OLS is biased and inconsistent when applied to a structural equation in a
simultaneous equations system.

18. The instrumental variables in the two stage least squares estimation method
consists of endogenous variables appearing in either equation.

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Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Identifying and Estimating a Structural Equation
BUSPROG:
Feedback: The instrumental variables in the two stage least squares method
consists of exogenous variables appearing in either equation.

19. The order condition is a necessary and sufficient condition for identification of
an equation in a simultaneous equations model.

Answer: False.
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Systems with More than Two Equations
BUSPROG:
Feedback: The order condition is a necessary condition for identification of an
equation in a simultaneous equations model. It is not a sufficient condition for
identification.

20. If a structured model contains a time trend—which may capture exogenous,

trending factors that are not directly modeled—then the trend acts as its own
instrumental variable.

Answer: True
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Simultaneous Equations Models with Time Series
BUSPROG:
Feedback: If a structured model contains a time trend, then the trend acts as its
own instrumental variable.

Chapter 17

1. Which of the following is an example of a binary response model?

a. MA model
b. ARCH model
c. GARCH model
d. Logit model

Answer: d
Difficulty: Easy
Bloom’s: Knowledge

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A-Head: Specifying Logit and Probit Models

BUSPROG:
Feedback: The logit model is an example of a binary response model.

2. The model: G(z) = [exp(z)]/[1 + exp(z)],where G is between zero and one for all
real numbers ‘z’, represents a:
a. logit model.
b. probit model.
c. Tobit model.
d. linear probability model.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Specifying Logit and Probit Models
BUSPROG:
Feedback: The following model: G(z) = [exp(z)]/[1 + exp(z)], where G is between
zero and one for all real numbers ‘z’, represents a logit model.

3. The model: G(z) = ∫ ø ( v ) dv where ø(z) = (2π)-1/2exp(-z2/2) represents a:

−∞

a. Tobit model.
b. logit model.
c. probit model.
d. linear probability model.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Specifying Logit and Probit Models
BUSPROG:
z

Feedback: The following model G(z) = ∫ ø ( v ) dv where ø(z) = (2π)-1/2exp(-z2/2)

−∞

represents a probit model.

4. The likelihood ratio statistic is given by:

a. LR = (log-likelihoodunrestricted + log-likelihoodrestricted)
b. LR = 2 × (log-likelihoodunrestricted + log-likelihoodrestricted)
c. LR = (log-likelihoodunrestricted – log-likelihoodrestricted)
d. LR = 2 × (log-likelihoodunrestricted – log-likelihoodrestricted)

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Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Specifying Logit and Probit Models
BUSPROG:
Feedback: The likelihood ratio statistic is given by LR = 2 × (log-likelihood unrestricted –
log-likelihoodrestricted)

5. The _____ model is designed to model corner solution dependent variables.

a. linear probability
b. logit
c. probit
d. Tobit

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Tobit Model for Corner Solution Responses
BUSPROG:
Feedback: The Tobit model is designed to model corner solution dependent
variables.

6. The model: y* = β0 + xβ + u, given u|x ~ Normal(0, σ2) and y = max(0, y*)

represents a:
a. ARCH model.
b. GARCH model
c. Tobit model
d. logit model

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Tobit Model for Corner Solution Responses
BUSPROG:
Feedback: The model: y* = β0 + xβ + u, given u|x ~ Normal(0, σ2) and y = max(0,
y*) represents a Tobit model.

7. Which of the following tests can be used to test hypotheses with multiple
restrictions under a Tobit model?
a. White test
b. Wald test
c. Dickey Fuller test
d. Durbin Watson test

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Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Tobit Model for Corner Solution Responses
BUSPROG:
Feedback: The Wald test can be used to check for multiple restrictions under a Tobit
model.

8. A count variable refers to a dependent variable that can take on:

a. nonnegative integer values.
b. nonnegative fractional values.
c. negative fractional values.
d. negative integer values.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Poisson Regression Model
BUSPROG:
Feedback: A count variable refers to a dependent variable that can take on
nonnegative integer values.

9. Which of the following statements is true?

a. Taking logarithmic of a count variable is a suitable way to model it.
b. All standard count data distributions exhibit heteroskedasticity.
c. The nonlinear least squares estimation aims at maximizing R 2.
d. Count variables cannot take on the value zero.

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: The Poisson Regression Model
BUSPROG:
Feedback: All standard count data distributions exhibit heteroskedasticity.

10. The nominal distribution for count data is the:

a. binomial distribution.
b. normal distribution.
c. Poisson distribution.
d. Bernoulli distribution.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge

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A-Head: The Poisson Regression Model

BUSPROG:
Feedback: The nominal distribution for count data is the Poisson distribution.

11. Which of the following statements is true?

a. A probit or logit model should be used for corner solution outcomes, and a
Poisson regression model should be used for a binary response.
b. A Poisson regression model should be used for corner solution outcomes, and a
probit or logit model should be used for a binary response.
c. A probit or logit model should be used for count variables, and a Poisson
regression model should be used for a binary response.
d. A Poisson regression model should be used for count variables, and a probit or
logit model should be used for a binary response.

Answer: d
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Censored and Truncated Regression Models
BUSPROG:
Feedback: A Tobit model should be used for corner solution outcomes, a Poisson
regression model should be used for count variables, and a probit or logit model
should be used for a binary response.

12. Which of the following statements is true?

a. OLS estimates in censored regression models are consistent estimators of the
population coefficients.
b. In a truncated regression model, the samples are not included randomly from an
underlying population but are based on a given rule.
c. In a censored regression model, units in the sample are taken from a particular
subset of the population.
d. Maximum likelihood estimators are consistent in truncated regression models
even if there is nonnormality or heteroskedasticity in the error terms.

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Censored and Truncated Regression Models
BUSPROG:
Feedback: In a truncated regression model, we do not have a random sample from
the underlying population, but we know the rule that was used to include units in
the sample. This rule is determined by whether the dependent variable is above or
below a certain threshold.

13. Duration is a variable that measures:

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a. the time when a certain event occurs.

b. the time before a certain event occurs.
c. the time after a certain event occurs.
d. the appropriate number of lags for a regression model.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Censored and Truncated Regression Models
BUSPROG:
Feedback: Duration is a variable that measures the time before a certain event
occurs.

14. Which of the following statements is true?

a. A truncated regression is a special case of a random sample selection.
b. Nonrandom sample selection can arise in cases of cross-sectional and time series
data, but not in the case of panel data.
c. The Tobit regression model is based on endogenous sample selection.
d. The censored regression model is based on nonrandom sample selection.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Sample Selection Corrections
BUSPROG:
Feedback: The Tobit regression model is based on endogenous sample selection.

15. Which of the following is a method to correct for sample selection bias for the
problem of incidental truncation?
a. Vector error correction method
b. First differencing method
c. Heckman’s method
d. Johansen method

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Sample Selection Corrections
BUSPROG:
Feedback: Heckman’s method can be used for correcting sample selection bias for
the problem of incidental truncation.

16. The cumulative distribution function for a standard logistic random variable is a
decreasing function.

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Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Specifying Logit and Probit Models
BUSPROG:
Feedback: The cumulative distribution function for a standard logistic random
variable is an increasing function.

17. The Tobit model relies crucially on normality and heteroskedasticity in the
underlying latent variable model.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Tobit Model for Corner Solution Responses
BUSPROG:
Feedback: The Tobit model relies crucially on normality and homoskedasticity in the
underlying latent variable model. Under heteroskedasticity, using a Tobit model is
inefficient.

18. In the Poisson regression model, the probability distribution is given by P(y = h|
x) = exp[-exp(xβ)][exp(xβ)]h/h!, h = 0, 1, …..

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: The Poisson Regression Model
BUSPROG:
Feedback: In the Poisson regression model, the probability distribution is given by
P(y = h|x) = exp[-exp(xβ)][exp(xβ)]h/h!, h = 0, 1, …..

19. When a variable is top coded, its value is known only up to a certain threshold.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Censored and Truncated Regression Models
BUSPROG:
Feedback: When a variable is top coded, its value is known only up to a certain
threshold. For responses greater than the threshold, it is only known that the
variable is at least as large as the threshold.

20. In case of endogenous sample selection, OLS is unbiased but consistent.

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Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Sample Selection Corrections
BUSPROG:
Feedback: In case of endogenous sample selection, OLS is unbiased and
inconsistent.
Chapter 18

1. Let {(yt, zt): t = …, 2, 1, 0, 1, 2, …} be a bivariate time series process. The
model: yt = α + βozt + β1zt – 1 + β2zt – 2 + ….. + ut, where t = …..,-2,-1,0,1,2,……,
represents a(n):
a. moving average model.
b. ARIMA model.
c. finite distributed lag model.
d. infinite distributed lag model.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Infinite Distributed Lag Models
BUSPROG:
Feedback: The model: yt = α + βozt + β1zt – 1 + β2zt – 2 + ….. + ut, where t = 0,1,2,……,
represents an infinite distributed lag model relating y t to all current and past values
of z.

2. The Koyck distributed lag model is an example of:

a. a moving average model.
b. an autoregressive conditional heteroskedasticity model.
c. an infinite distributed lag model.
d. a finite distributed lag model.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Infinite Distributed Lag Models
BUSPROG:
Feedback: The Koyck distributed lag model is an example of an infinite distributed
lag model.

3. The model: yt = α0 + γ0zt +ρyt – 1 + γ1zt – 1 +vt, where vt = ut – ρut – 1 represents a:

a. finite distributed lag model.
b. simultaneous equations model.

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c. rational distributed lag model.

d. vector error correction model.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Infinite Distributed Lag Models
BUSPROG:
Feedback: The model: yt = α0 + γ0zt +ρyt – 1 + γ1zt – 1 +vt, where vt = ut – ρut – 1 -
represents a rational distributed lag model.

4. In the given AR(1) model, yt = α + ρyt – 1, t = 1,2…… , the Dickey-Fuller

distribution refers to the:
a. asymptotic distribution of the t statistic under the hypothesis ρ – 1 = 0.
b. asymptotic distribution of the F statistic under the hypothesis ρ – 1 = 0.
c. asymptotic distribution of the χ2 statistic under the hypothesis ρ – 1 = 0.
d. asymptotic distribution of the z statistic under the hypothesis ρ – 1 = 0.

Answer: a
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Testing for Unit Roots
BUSPROG:
Feedback: In the given model: y t = α + ρyt – 1, t = 1,2…… , the Dickey-Fuller
distribution refers to the asymptotic distribution of the t statistic under the
hypothesis ρ – 1 = 0.

5. Which of the following is used to test whether a time series follows a unit root
process?
a. Wald test
b. White test
c. Augmented Dickey-Fuller test
d. Johansen test

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Testing for Unit Roots
BUSPROG:
Feedback: The augmented Dickey-Fuller test can be used to check for unit root in a
time series

6. A spurious correlation refers to a situation where:

a. two variables are related through their correlation with a third variable.

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b. the correlation coefficient between two variables cannot be estimated.

c. there is direct causal relationship between two variables but tests for correlations
reject this relationship.
d. the correlation between two variables is positive till the sample size reaches a
threshold, and negative after the sample size crosses the threshold.

Answer: a
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Spurious Regression
BUSPROG:
Feedback: A spurious correlation refers to a situation where two variables are
related through their correlation with a third variable.

7. A spurious regression refers to a situation where:

a. the direction of the relationship between the dependent variable and the
explanatory variables is uncertain.
b. even though two variables are independent, the OLS regression of one variable
on the other indicates a relationship between them.
c. a few important and necessary explanatory variables are left out of a regression
equation, thus leading to inefficient and inconsistent forecasts.
d. at least one of the variables used in a regression equation does not have a unit
root and the error terms are heteroskedastic.

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Spurious Regression
BUSPROG:
Feedback: A spurious regression refers to a situation where even though two
variables are independent, the OLS regression of one variable on the other indicates
a relationship between them.

8. Which of the following statements is true of spurious regressions?

a. The OLS estimates of the population parameters are efficient and unbiased and
the t statistic is valid.
b. Even if the explanatory variables and the dependent variable are independent
times series processes, the R2 can large.
c. Spurious regressions are limited to I(0) processes, and are not possible in case of
I(1) processes.
d. Spurious regressions are limited to I(1) processes, and are not possible in case of
I(0) processes.

Answer: b

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Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Spurious Regression
BUSPROG:
Feedback: In case of a spurious regression, even if the explanatory variables and
the dependent variable are independent times series processes the R 2 can large.

9. Two series are said to be cointegrated if:

a. both series are I(1) but a linear combination of them is I(0).
b. both series are I(0) but a linear combination of them is I(1).
c. both series have the same set of explanatory variables but a different dependent
variable.
d. both series have the same dependent variable but a different set of explanatory
variables.

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Cointegration and Error Correction Models
BUSPROG:
Feedback: Two series are said to be cointegrated if both series are I(0) but a linear
combination of them is I(1).

10. Which of the following tests can be used to check for cointegration between two
series?
a. Wald test
b. Breush-Pagan test
c. White test
d. Engle-Granger test

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Cointegration and Error Correction Models
BUSPROG:
Feedback: The Engle-Granger test can be used to check for cointegration between
two series.

11. Which of the following statements is true?

a. The calculated t statistic is valid and efficient in case of a spurious regression.
b. If an explanatory variable or a dependent variable is integrated of the order one,
the OLS estimators are asymptotically normally distributed.

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c. An error correction model can be used to study the short-run dynamics in the
relationship between the dependent variable and the explanatory variables in a
time series model.
d. The Dickey-Fuller test can be used to test for heteroskedasticity in the error
terms.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Cointegration and Error Correction Models
BUSPROG:
Feedback: An error correction model can be used to study the short-run dynamics in
the relationship between the dependent variable and the explanatory variables.

12. If ft denotes the forecast of yt+1 made at time t, then the forecast error is given
by:
a. et+1 = ft/yt+1.
b. et+1 = yt+1/ft.
c. et+1 = yt+1 + ft.
d. et+1 = yt+1 – ft.

Answer: d
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Forecasting
BUSPROG:
Feedback: If ft denotes the forecast of yt+1 made at time t, then the forecast error is
given by et+1 = yt+1 – ft.

13. Which of the following is true of squared forecast errors?

a. An error of +4 yields a greater loss than an error of -4.
b. An error of -4 yields a greater loss than an error of +4.
c. An error of -4 or +4 yields the same loss.
d. Loss from positive and negative forecast errors cannot be compared.

Answer: c
Difficulty: Easy
Bloom’s: Application
A-Head: Forecasting
BUSPROG: Analytic
Feedback: In case of squared forecast errors, an error of -2 or +2 yields the same
loss.

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14. Which of the following statements correctly identifies the difference between an
autoregressive model and a vector autoregressive model?
a. In an autoregressive model, the dependent variable is expressed as a function of
its own lag, whereas in a vector autoregressive model, the dependent variable is
expressed as a function of the lag of an explanatory variable.
b. In an autoregressive model, the dependent variable is expressed as a function of
the lag of an explanatory variable, whereas in a vector autoregressive model, the
dependent variable is expressed as a function of its own lag.
c. In an autoregressive model several series are modelled in terms of their own
past, whereas in a vector autoregressive model only one series is modelled in terms
of its own past.
d. In an autoregressive model one series is modelled in terms of its own past,
whereas in a vector autoregressive model several series are modelled in terms of
their past.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Forecasting
BUSPROG:
Feedback: In an autoregressive model one series is modelled in terms of its own
past, whereas in a vector autoregressive model several series are modelled in terms
of their past.

15. In case of forecasts, the root mean squared error is the:

a. average of the forecast errors divided by the variance of the errors.
b. average of the absolute forecast errors.
c. standard deviation of the forecast errors without any degrees of freedom
adjustment.
d. standard deviation of the forecast errors with a degrees of freedom adjustment.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Forecasting
BUSPROG:
Feedback: In case of forecasts, the root mean squared error is the standard
deviation of the forecast errors without any degrees of freedom adjustment.

16. If the t statistic for the presence of a unit root in a variable is -7.22 and the 5%
critical value is -2.86, there is strong evidence against a unit root in the variable.

Answer: True
Difficulty: Easy

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Bloom’s: Application
A-Head: Testing for Unit Roots
BUSPROG: Analytic
Feedback: Since the absolute value of the t statistic for the presence of a unit root in
a variable is greater than the absolute critical value, there is strong evidence
against a unit root in the variable.

17. The R2 calculated in a spurious regression is a valid and efficient estimate of the
goodness-of-fit of the regression equation.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Spurious Regression
BUSPROG:
Feedback: The R2 calculated in a spurious regression is not a valid and efficient
estimate of the goodness-of-fit of the regression equation as the calculated value
can be very high even if there is no relationship between the dependent variable
and the explanatory variables.

18. Exponential smoothing is a forecasting method where the weights on the lagged
dependent variable decline to zero exponentially.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Forecasting
BUSPROG
Feedback: Exponential smoothing is a forecasting method where the weight on the
lagged dependent variable decline to zero exponentially.

19. In calculation of squared forecast errors, an error of +3 yields a loss three times
greater than an error of -1.

Answer: False
Difficulty: Moderate
Bloom’s: Application
A-Head: Forecasting
BUSPROG: Analytic
Feedback: In calculation of squared forecast errors, an error of +3 yields a loss nine
times an error of -1.

20. Vector autoregressive models should be used for forecasting if the series being
studied are cointegrated.

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Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Forecasting
BUSPROG:
Feedback: Error correction models should be used for forecasting if the series being
studied are cointegrated.

Chapter 19

1. Which of the following is a characteristic of a good research question?

a. A good research question should have policy implications.
b. A good research question should be constrained to macroeconomics.
c. A good research question should be backed by available information.
d. A good research question should be subjective and not objective.

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Posing a Question
BUSPROG:
Feedback: A good research question should be backed by available information in
the form of data so that it can be answered.

2. Which of the following is a characteristic of a good research paper?

a. A good research paper should contain a review of relevant literature.
b. A good research paper should not have any published precedents.
c. A good research paper should always be useful for policy implications.
d. A good research paper should have more than one author.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Literature Review
BUSPROG:
Feedback: A good research paper should contain a review of relevant literature.

3. Which of the following statements is true of an appropriate data set?

a. An appropriate data set should be collected from government registered
websites.
b. An appropriate data set should have enough controls to do a reasonable ceteris
paribus analysis.
c. An appropriate data set should not have time series units.

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d. An appropriate data set should not be based on surveys.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Data Collection
BUSPROG:
Feedback: An appropriate data set should have enough controls to do a ceteris
paribus analysis.

4. The most flexible way to obtain data in electronic form is as a standard _____ file.
a. PDF
b. WMV
c. text (ASCII)
d. PPTX

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Data Collection
BUSPROG:
Feedback: The most flexible way to obtain data in electronic form is as a standard
text (ASCII) file.

5. Time series data should be stored:

a. with the earliest time period listed as the first observation, and the most recent
time period as the last observation.
b. with the earliest time period listed as the last observation, and the most recent
time period as the first observation.
c. with the time period in which the concerned variable takes the highest value
listed as the first observation, and the time period in which the concerned variable
takes the lowest value as the last observation.
d. with the time period in which the concerned variable takes the lowest value listed
as the first observation, and the time period in which the concerned variable takes
the highest value as the last observation.

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Data Collection
BUSPROG:
Feedback: Time series data should be stored with the earliest time period listed as
the first observation, and the most recent time period as the last observation.

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6. Which of the following correctly identifies an advantage of entering data in a

spreadsheet rather than a text file?
a. Data in various websites is more often available as spreadsheets than as text
files.
b. Different observations are more likely to be run together in spreadsheets than in
text files.
c. Spreadsheets are readable on most computers, whereas text files are not.
d. Spreadsheets allow manipulation of data, whereas text files do not.

Answer: d
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Data Collection
BUSPROG:
Feedback: Spreadsheets allow manipulation of data such as calculation of averages,
medians, etc. whereas text files do not.

7. Which of the following statements is true?

a. To carry out OLS estimation, it is necessary that the error terms should be
correlated with the explanatory variables.
b. Measurement error and simultaneity are potential sources of endogeneity.
c. While entering data for a regression analysis, qualitative explanatory variables
should be assigned numerical values.
d. AR models are best suited for ordered responses.

Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Econometric Analysis
BUSPROG:
Feedback: Measurement error and simultaneity are potential sources of
endogeneity.

8. Which of the following statements is true of stepwise regression?

a. In a stepwise regression, the dependent variable is regressed on each
independent variable individually with an attempt to understand the effect of each
independent variable on the dependent variable.
b. In a stepwise regression, the dependent variable is regressed on different
combinations of the independent variable with an attempt to come up with the best
model.
c. In a stepwise regression, the dependent variable can take up binary values only
while the explanatory variables can take up quantitative values.
d. In a stepwise regression, the dependent variable can take up quantitative values
while the explanatory variables can take up only binary values.

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Answer: b
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Econometric Analysis
BUSPROG:
Feedback: In a stepwise regression, the dependent variable is regressed on different
combinations of the independent variable with an attempt to come up with the best
model.

9. Which of the following statements is true?

a. Graphs should be avoided while introducing a research paper’s topic.
b. The introduction to a research paper should be longer than the conclusion to the
research paper.
c. The summary of a research paper can be presented in the introduction of a
research paper.
d. Statistics should be avoided in introductions for research papers.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Writing an Empirical Paper
BUSPROG:
Feedback: The summary of a research paper can be presented in the introduction of
a research paper as it helps grab the reader’s attention.

10. Which of the following statements is true?

a. OLS is a model, whereas feasible GLS is an estimation method.
b. OLS is an estimation method, whereas feasible GLS is a model.
c. Both OLS and feasible GLS are models.
d. Both OLS and feasible GLS are estimation methods.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Writing an Empirical Paper
BUSPROG:
Feedback: Both OLS and feasible GLS are methods to estimate models and not
models by themselves.

11. Which of the following statements is true?

a. A model shows the estimated parameters of the explanatory variables.
b. A model represents a population relationship.

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c. In an empirical paper, the estimation methods should be discussed before

specifying a model.
d. The methods for estimating a model are same as the model itself.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Writing an Empirical Paper
BUSPROG:
Feedback: A model represents a population relationship.

12. Which of the following highlights a requirement of a good instrumental variable?

a. It should be included in and endogenous to the equation of interest.
b. It should be included in and exogenous to the equation of interest.
c. It should be omitted from and exogenous to the equation of interest.
d. It should be omitted from and endogenous to the equation of interest.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Writing an Empirical Paper
BUSPROG:
Feedback: For efficient estimation, a good instrumental variable should be omitted
from and exogenous to the equation of interest.

13. Which of the following statements is true?

a. If a model has several explanatory variables and several variations to the general
model are to be presented, it is better to report the results in an equation form,
rather than a tabular form.
b. If a model has several explanatory variables and several variations to the general
model are to be presented, it is better to report the results in a tabular form, rather
than an equation form.
c. The adjusted R-squared should always be listed in the result section of a research
paper, while the R-squared may or may not be listed.
d. The number of observations for each equation should always be listed in the
result section of a research paper, while the R-squared may or may not be listed.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Writing an Empirical Paper
BUSPROG:

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Feedback: If a model has several explanatory variables and several variations to the
general model are to be presented, it is better to report the results in a tabular
form, rather than an equation form.

14. Which of the followings statements is true?

a. The title of a research paper should be mentioned only in the introduction
section.
b. The title page should not include any other information except the title of the
research paper.
c. Graphs and tables should be included in the main body only.
d. All equations should begin on a new line and should be numbered consecutively.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Writing an Empirical Paper
BUSPROG:
Feedback: All equations used in a research paper should begin on a new line and
should be numbered consecutively.

15. Which of the following statements is true?

a. If standard errors are too large or too small, they should be reported in scientific
notation.
b. If coefficients are too large or too small, they should be reported in scientific
notation.
c. While reporting figures, the number of digits after decimals should be limited.
d. The list of references used should be reported in the appendix.

Answer: c
Difficulty: Moderate
Bloom’s: Knowledge
A-Head: Writing an Empirical Paper
BUSPROG:
Feedback: While reporting figures in research papers, the number of digits after
decimals should be limited so as not to convey a false sense of precision.

16. A good research question should not be backed by time series data.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Posing a Question
BUSPROG:

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Feedback: A good research question can be backed by time series data. Most of the
important macroeconomic researches are based on time series data.

17. Historical data sets are available only in printed form.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Data Collection
BUSPROG:
Feedback: Historical data sets are available only in printed form.

18. The practice of data mining is consistent with the assumptions made in
econometric analysis.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Econometric Analysis
BUSPROG:
Feedback: The practice of data mining is violates the assumptions on which
econometric analysis is based.

19. Instrumental variables can be used to solve misspecification errors related to

omitted variables.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Econometric Analysis
BUSPROG:
Feedback: Instrumental variables can be used to solve misspecification errors
related to omitted variables.

20. Sensitivity analysis is restricted to natural sciences.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Econometric Analysis
BUSPROG:
Feedback: Sensitivity analysis can be used for social sciences as well.

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Economic and Financial Modelling Quiz Bank

Economic and Financial Modelling (Western Sydney University)

Studocu is not sponsored or endorsed by any college or university

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1. Module 01 - The Nature of Econometrics and

Economic Data
1st Attempt

 Question 1
0 out of 1 points
Let the variable Y be normally distributed with mean 12 and variance 16. That
is, Y ~ N(12, 16). What is the prob. (7 ≤Y≤ 22.2)?

Selected b.
Answer: 0.487
8
Answers: a.
0.894
4
b.
0.487
8

c.
0.889
0
d.
0.994
6
 Question 2
1 out of 1 points
Which of the following is an example of time series data?
Selected
Answer: Data on the gross domestic product of a country over a period
of 10 years.
Answers: Data on the unemployment rates in different parts of a country during a
year.
Data on the consumption of wheat by 200 households during a year.

Data on the gross domestic product of a country over a period

of 10 years.

Data on the number of vacancies in various departments of an

organisation in a particular month.
 Question 3
0 out of 1 points
Which of the following is a type of economic data based on how the data is
structured?

Selected b.
Answers: cross-sectional

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data
c.
panel data
Answers: a.
digital data
b.
cross-sectional
data
c.
panel data
d.
time series data
 Question 4
1 out of 1 points
Which of the following refers to panel data?
Selected
Answer: Data on the birth rate, death rate and population growth rate in developing
countries over a 10-year period.
Answers: Data on the income of five members of a family on a particular year.
Data on the price of a company’s share during a year.

Data on the birth rate, death rate and population growth rate in developing
countries over a 10-year period.
Data on the unemployment rate in a country over a 5-year period
 Question 5
1 out of 1 points
Suppose that the salary of new finance graduates in Australia with analytical
skills is normally distributed with unknown mean μ in A$ and variance. Suppose a
sample of 25 business graduates is drawn and the sample mean is observed as
X= A$60,000 with sample standard deviation s=$1,800. What would you
conclude based on the null hypothesis, Ho: μ= A$61,200 against the alternative
that H1: μ≠ A$61 200 at the 5% level (2.5% in each tail)?

Selected b.
Answer: Reject the null hypothesis at 5% level.
Answers: a.
Do not reject the null hypothesis.

b.
Reject the null hypothesis at 5% level.
c.
The null hypothesis is statistically
significant.

d.
Need more information to make a
decision.
 Question 6
1 out of 1 points

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Which of the following options is a data set that consists of a sample of

individuals, households, organisations, cities, states, countries, or a variety of
other units, taken at a given point in time?
Selected
Answer: Cross-section data
set.
Answers: Experimental data
set.
Time series data
set.

Cross-section data
set.
Longitudinal data
set
 Question 7
0 out of 1 points
Which of the following is not an appropriate example of longitudinal
data? There is more than one.
Selected
Answers: Total loans disbursed recorded by the National Australia Bank and
Westpac Bank in the first quarter of 2016; and then the same
information collected for ANZ Bank and the Commonwealth Bank in
the third quarter of the year.

The annual net profit of 35 Australian clothing firms compiled from

2004 to 2010
Answers:
The annual consumption expenditure of 50 Australian households in
2015

Total loans disbursed recorded by the National Australia Bank and

Westpac Bank in the first quarter of 2016; and then the same
information collected for ANZ Bank and the Commonwealth Bank in
the third quarter of the year.
The annual net profit of 35 Australian clothing firms compiled from
2004 to 2010

Marks obtained in econometrics by 50 undergraduate students of

Monash University in the first semester of 2015
Answer This is a pooled-cross sectional data as those sample are different in
Feedback: each time period, however the same variables were being collected.

Answer This is longitudinal data as the variables collected were for the same
Feedback: members of observation for over a period of time.

Monday, 27 July 2020 8:40:00 PM AEST

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Module 02 - Introduction to bivariate regression

(Simple Linear Regression)

 Question 1
1 out of 1 points
Which of the following options is a data set that consists of a sample of
individuals, households, organisations, cities, states, countries, or a variety of
other units, taken at a given point in time?
Selected
Answer: Cross-section data
set.
Answers: Time series data
set.
Experimental data
set.
Longitudinal data
set

Cross-section data
set.
 Question 2
1 out of 1 points
Which of the following is an example of time series data?
Selected
Answer: Data on the gross domestic product of a country over a period

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of 10 years.
Answers: Data on the unemployment rates in different parts of a country during a
year.
Data on the consumption of wheat by 200 households during a year.

Data on the gross domestic product of a country over a period

of 10 years.

Data on the number of vacancies in various departments of an

organisation in a particular month.

 Question 3
1 out of 1 points
Suppose you are given the Excel output in Figure 4.1 which shows the relationship
between annual earnings of PGA tour players (Earnings) and driving distance
(Yards Per Drive) as per model below:

You would conclude that each additional Yard Per Drive is estimated to be associated with a:

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Selected
Answer: $30,737.523 increase in annual earnings.
Answers: $2,867,254.773 increase in annual earnings.
$9,823.548 increase in annual earnings.

$30,737.523 increase in annual earnings.

$7,773,135.558 decrease in annual earnings.

 Question 4
1 out of 1 points
Which of the following refers to panel data?
Selected
Answer: Data on the birth rate, death rate and population growth rate in developing
countries over a 10-year period.
Answers: Data on the price of a company’s share during a year.

Data on the birth rate, death rate and population growth rate in developing
countries over a 10-year period.
Data on the income of five members of a family on a particular year.
Data on the unemployment rate in a country over a 5-year period

 Question 5
1 out of 1 points
Suppose you determine the estimated sample regression function to be:

You would conclude that:

Selected
Answer:
is estimated to equal 687.51 when =0

Answers: is estimated to equal 3,212.97 when = 0.

is estimated to decrease by 687.51 for every one unit

increase in .

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is estimated to equal 687.51 when =0

is estimated to increase by 3,212.97 for every one unit increase

in .

 Question 6
1 out of 1 points
Suppose you regress the number of days that individuals report having
hunted in the past year on years of education as per model:

And that you get the results in Figure 4.3:

You would conclude that for every extra year of education, the number of days
hunted would:

Selected
Answer:
Drop by 0.2969 days spent on hunting
Answers:
Drop by 0.2969 days spent on hunting
Drop by 0.2358 days spent on hunting
Drop by 0.3580 days spent on hunting
Rise by 0.0312 days spent on hunting
 Question 7

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1 out of 1 points
The parameters of an econometric model:
Selected
Answer: describe the strength of the relationship between the variable under study
and the factors affecting it.
Answers: refer to the explanatory variables included in the model.

describe the strength of the relationship between the variable under study
and the factors affecting it.
include all unobserved factors affecting the variable being studied.
refer to the predictions that can be made using the model.
 Question 8
1 out of 1 points
Consider the following regression model: y = β0 + β1x1 + u. Which of the
following is a property of ordinary least square (OLS) estimates of this
model and their associated statistics?
Selected
Answer:
The point , always lies on the OLS regression line.

Answers: The sum, and therefore the sample average of the OLS
residuals, is positive.
The sum of the OLS residuals is negative.

The point , always lies on the OLS regression line.

The sample covariance between the regressors and the OLS

residuals is positive.

 Question 9
1 out of 1 points

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Suppose that you regress the total number of medals that a country won in the
2008 Olympics on GDP per capita (in thousands) as shown in:

And that you get the results in Figure 4.2.

You would conclude that for every one thousand rise in GDP per capita, the
number of medals would:

Selected
Answer:
Rise by 0.1553 medals

Answers: Drop by 2.3079 medals

Drop by 6.0792 medals

Rise by 0.1553 medals

Rise by 0.0787 medals

 Question 10
1 out of 1 points

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Refer to the following equation:

Qs = β1 + β2P + β3Pr + β4Ps + β5TAX + e
where Qs is annual quantity supplied, P is the price of the product, Pr is the
price of resources, Ps is the price of goods that are substitutes in
production, and TAX is the excise tax on the product. In this
equation β1 represents

Selected
Answer: a parameter to be
estimated
Answers:
a parameter to be
estimated
the predicted quantity
supplied
the equilibrium quantity
the random error term

Module 03 - Multiple Regression Analysis: Estimation

 Question 1
1 out of 1 points
What is high (but not perfect) correlation between two or more independent
variables called?
Selected c.
Answer: Multicollinearity
Answers: a.
Heteroskedasticity
b.
Homoskedasticity

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c.
Multicollinearity
d.
Micronumerosity
 Question 2
1 out of 1 points
The "holding all other independent variables constant" condition is important

Selected c.
Answer:
to ensure that we are correctly estimating marginal effects.

Answers: a.
to ensure that the error term is correlated with the independent variables.
b.
because economists want to know how a change in the dependent
variable affects the independent variable.

c.
to ensure that we are correctly estimating marginal effects.

d.
because it comes at the end of every definition in economics.

 Question 3
1 out of 1 points
In the multiple regression model which of the following does NOT lead to larger
variances of the least squares estimators b2 and var(b2)?
Selected a.
Answer:
larger correlation between

and

Answers: a.
larger correlation between

and

b.

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larger error variances,

c.
larger correlation between

and

d.
smaller values of Ʃ(xi2 - x̅ 2)2
 Question 4
1 out of 1 points
Which of the following is true of R2?

Selected a.
Answer: R2 shows what percentage of the total variation in the dependent
variable, Y, is explained by the explanatory variables.

Answers: a.
R2 shows what percentage of the total variation in the dependent
variable, Y, is explained by the explanatory variables.

b.
R2 is also called the standard error of regression.

c.
R2 usually decreases with an increase in the number of
independent variables in a regression.

d.
A low R2 indicates that the Ordinary Least Squares line fits the data
well.

 Question 5
1 out of 1 points

Find the degrees of freedom in a regression model that has 10 observations and
7 independent variables.

Selected a.
Answer: 2
Answers: a.

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2
b.
3
c.
4
d.
17

 Question 6
1 out of 1 points
If an independent variable in a multiple linear regression model is an exact linear
combination of other independent variables, the model suffers from which
problem?

Selected a.
Answer: Perfect collinearity
Answers: a.
Perfect collinearity
b.
Heteroskedasticity
c.
Omitted variable bias
d.
Homoskedasticity
 Question 7
1 out of 1 points
How should in the general multiple regression model be interpreted?

Selected b.
Answer:
The number of units of change in the expected value of y for a 1
unit increase in when all remaining variables are

unchanged

Answers: a.
The number of variables used in the model.

b.
The number of units of change in the expected value of y for a 1
unit increase in when all remaining variables are

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unchanged

c.
The amount of variation in y explained by in the model

d.
The magnitude by which varies in the model

 Question 8
1 out of 1 points
Why is the adjusted R-squared is used?

Selected a.
Answer: Because it imposes a penalty for adding additional independent variables
to a model.
Answers: a.
Because it imposes a penalty for adding additional independent variables
to a model.
b.
Because it increases the value of the adjusted R-squared.
c.
Because it is unbiased unlike the R-squared measure.
d.
Because it is better than the R-squared measure.
 Question 9
1 out of 1 points
In multiple regression, the increases whenever a regressor is

Selected d.
Answer:
added unless the coefficient on the added regressor is exactly
zero.

Answers: a.
greater than 1.96 in absolute value.

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b.
added.

c.
added unless there is heterosckedasticity.

d.
added unless the coefficient on the added regressor is exactly
zero.

 Question 10
1 out of 1 points
Consider the following regression equation: y = β1 + β2 x1 + β2 x2 + u. What does β1 imply?

Selected c.
Answer: β1 measures the ceteris paribus effect of x1 on y.
Answers: a.
β1 measures the ceteris paribus effect of x1 on x2.
b.
β1 measures the ceteris paribus effect of y on x1.
c.
β1 measures the ceteris paribus effect of x1 on y.
d.
β1 measures the ceteris paribus effect of x1 on u.
1. Module 04 - Multiple Regression Analysis: Inference

 Question 1
0 out of 1 points
Consider the equation, Y = β1 + β2x2 + u. A null hypothesis, H0: β2 = 0 states that:

Selected a.
Answer: β2 has no effect on the expected value of Y.
Answers: a.
β2 has no effect on the expected value of Y.
b.
x2 has no effect on the expected value of β2.
c.
x2 has no effect on the expected value of Y.
d.

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Y has no effect on the expected value of X 2.

 Question 2
0 out of 1 points

What does the normality assumption imply?

Selected a.
Answer: That the population error u is dependent on the explanatory variables and
is normally distributed with mean zero and variance σ.
Answers: a.
That the population error u is dependent on the explanatory variables and
is normally distributed with mean zero and variance σ.
b.
That the population error u is independent of the explanatory variables and
is normally distributed with mean equal to one and variance σ.
c.
That the population error u is independent of the explanatory variables and
is normally distributed with mean zero and variance σ 2.
d.
That the population error u is dependent on the explanatory variables and is
normally distributed with mean equal to one and variance σ 2.
 Question 3
0 out of 1 points
Supposed you regress the hourly wage (wage) of employees on their years of
education (educ), years of experience (exper) and years working with the
company (tenure) estimating the model:

And obtaining the results below from STATA.

. regress wage educ exper tenure

Source | SS df MS Number of obs = 526

-------------+------------------------------ F( 3, 522) = 76.87

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Model | 2194.11162 3 731.370541 Prob > F = 0.0000

Residual | 4966.30269 522 9.51398982 R-squared = 0.3064

-------------+------------------------------ Adj R-squared = 0.3024

Total | 7160.41431 525 13.6388844 Root MSE = 3.0845

------------------------------------------------------------------------------

wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------

educ | .5989651 .0512835 11.68 0.000 .4982176 .6997126

exper | .0223395 .0120568 1.85 0.064 -.0013464 .0460254

tenure | .1692687 .0216446 7.82 0.000 .1267474 .

2117899

_cons | -2.872735 .7289643 -3.94 0.000 -4.304799 -1.440671

------------------------------------------------------------------------------

Based on the estimates from STATA, you should conclude that holding all other
independent constant, each additional year of education is estimated to be
associated with:
Selected b.
Answer: a statistically insignificant increase of $0.59 in the hourly wage
Answers: a.
a statistically significant increase of 59.8% percent in the hourly wage
b.
a statistically insignificant increase of $0.59 in the hourly wage

c.

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a statistically significant increase of $0.59 in the hourly wage

d.
a statistically insignificant increase of 59.8% percent in the hourly
wage
 Question 4
1 out of 1 points
When testing joint hypothesis, you should

Selected a.
Answer:
use the F-statistics and reject at least one of the hypothesis if
the statistic exceeds the critical value.
Answers: a.
use the F-statistics and reject at least one of the hypothesis if
the statistic exceeds the critical value.
b.
use t-statistics for each hypothesis and reject the null hypothesis
once the statistic exceeds the critical value for a single
hypothesis.
c.
use t-statistics for each hypothesis and reject the null hypothesis
is all of the restrictions fail.
d.
use the F-statistic and reject all the hypothesis if the statistic
exceeds the critical value.
 Question 5
0 out of 1 points
The general t statistic can be written as:

Selected d.
Answer:

Answers: a.

b.

c.

d.

 Question 6
0 out of 1 points

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Which of the following is not a component of a hypothesis test?

Selected c.
Answer:
null hypothesis

Answers: a.
rejection region

b.
goodness-of-fit

c.
null hypothesis

d.
test statistic

 Question 7
0 out of 1 points
When should a left-tailed significance test be used?

Selected b.
Answer:
When you know the true value of is positive

Answers: a.
When economic theory suggests the coefficient should be
positive

b.
When you know the true value of is positive

c.
When it allows you to reject the null hypothesis at a lower p-
value

d.
When economic theory suggests the coefficient should be
negative

 Question 8
0 out of 1 points
Suppose that you collected data from households and you obtained the total
spent on apples in dollars (regbought), their family income in thousands
(faminc), price of the apples per kilo (regprcKG) and an indicator variable
whether the data was collected when apples were in season or not (inseason).
The model you estimated was:

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The results you obtained from STATA are shown below.

. regress regbought faminc regprcKG inseason

Source | SS df MS Number of obs = 660

-------------+------------------------------ F( 3, 656) = 1.31
Model | 21.1606503 3 7.0535501 Prob > F = 0.2717
Residual | 3545.32241 656 5.4044549 R-squared = 0.0059
-------------+------------------------------ Adj R-squared = 0.0014
Total | 3566.48306 659 5.41196216 Root MSE = 2.3247

------------------------------------------------------------------------------
regbought | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
faminc | .0002103 .0025392 0.08 0.934 -.0047755 .
0051962
regprcKG | .2904129 .1683306 1.73 0.085 -.0401187 .
6209446
inseason | .1986479 .1919084 1.04 0.301 -.1781809 .
5754767
_cons | .4476677 .3778522 1.18 0.237 -.2942779
1.189613
------------------------------------------------------------------------------
Based on the estimates from STATA, you should conclude that holding all other
independent constant, each additional thousand in family income is estimated to
be associated with:

Selected c.
Answer:
a statistically significant increase of 0.02103% in apples
bought.
Answers: a.
a statistically insignificant increase of $0.0002103 in apples
bought.

b.
a statistically significant increase of $0.0002103 in apples
bought.

c.
a statistically significant increase of 0.02103% in apples
bought.
d.
a statistically insignificant increase of 0.02103% in apples
bought.
 Question 9
0 out of 1 points

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Supposed you regress the hourly wage (wage) of employees on their years of
education (educ), years of experience (exper) and years working with the
company (tenure) estimating the model:

And obtaining the results below from STATA.

. regress wage educ exper tenure

Source | SS df MS Number of obs = 526

-------------+------------------------------ F( 3, 522) = 76.87

Model | 2194.11162 3 731.370541 Prob > F = 0.0000

Residual | 4966.30269 522 9.51398982 R-squared = 0.3064

-------------+------------------------------ Adj R-squared = 0.3024

Total | 7160.41431 525 13.6388844 Root MSE = 3.0845

------------------------------------------------------------------------------

wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------

educ | .5989651 .0512835 11.68 0.000 .4982176 .6997126

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exper | .0223395 .0120568 1.85 0.064 -.0013464 .0460254

tenure | .1692687 .0216446 7.82 0.000 .1267474 .

2117899

_cons | -2.872735 .7289643 -3.94 0.000 -4.304799 -1.440671

------------------------------------------------------------------------------

Based on the estimates from STATA, you should conclude that holding all other
independent constant, each additional year of experience is estimated to be
associated with:
Selected b.
Answer: a statistically insignificant increase of 2.23 percent in the hourly wage
Answers: a.
a statistically significant increase of 2.23 percent in the hourly wage
b.
a statistically insignificant increase of 2.23 percent in the hourly wage
c.
a statistically significant increase of $0.02 in the hourly wage

d.
a statistically insignificant increase of $0.02 in the hourly wage
 Question 10
0 out of 1 points
In which case would testing the null hypothesis involve a two-tailed statistical
test?

Selected b.
Answer:
: Extending the duration of unemployment benefits does

not increase the length of joblessness

Answers: a.
: Higher sales tax rates does not reduce state tax

revenues
b.
: Extending the duration of unemployment benefits does

not increase the length of joblessness

c.

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: Incentive pay for teachers does affect student

achievement
d.
: Smoking does not reduce life expectancy

1. Module 06 - Multiple Regression Analysis with Qualitative

Information: Binary(or Dummy) Variables

 Question 1
1 out of 1 points

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If an independent variable in a multiple linear regression model is an exact linear

combination of other independent variables, the model suffers from the problem
of _____________.

Selected
Answer:
perfect collinearity

Answers:
perfect collinearity

omitted variable bias

homoskedasticity

heteroskedasticty

 Question 2
1 out of 1 points
Running auxillary regressions where each explanatory variable is estimated as a
function of the remaining explanatory variables can help detect.

Selected
Answer:
collinearity

Answers: irrelevant variables

included

heteroskedasiticity

omitted relevant variables

collinearity

 Question 3
1 out of 1 points
You estimate 4 different specifications of an econometric model by adding a
variable each time and get the following results

R2 adj R2 AIC
0.345
Model A 8 0.3285 22.56
0.368
Model B 9 0.3394 22.37
0.425
Model C 6 0.3916 21.21
Model D 0.429 0.3911 21.79

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Which model appears to be correctly specified?

Selected
Answer: C
Answers: A
B

C
D
 Question 4
1 out of 1 points
If your regression results show a high R2 , adj R2, and a significant F-test, but low
t values for the coefficients, what is the most likely cause?

Selected
Answer:
collinearity

Answers:
collinearity

omitted relevant variables

irrelevant variables
included

heteroskedasiticity

 Question 5
1 out of 1 points
How does including an irrelevant variable in a regression model affect the
estimated coefficient of other variables in the model?

Selected
Answer:
they are unbiased but have larger standard errors

Answers:
they are unbiased but have larger standard errors

they are biased upward and have larger standard errors

they are biased and the bias can be negative or positive

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they are biased downward and have smaller standard errors

 Question 6
1 out of 1 points
When collinear variables are included in an econometric model coefficient
estimates are

Selected
Answer:
unbiased but have larger standard errors

Answers:
unbiased but have larger standard errors

biased and the bias can be negative or positive

biased upward and have larger standard errors

biased downward and have smaller standard errors

 Question 7
1 out of 1 points
Which of the following is true of Regression Specification Error Test (RESET)?

Selected
Answer:
It tests if the functional form of a regression model is
misspecified.

Answers: It helps in the detection of multicollinearity among the

independent variables in a regression model.

It tests if the functional form of a regression model is

misspecified.

It detects the presence of dummy variables in a regression

model.

It helps in the detection of heteroskedasticity when the

functional form of the model is correctly specified.

 Question 8
1 out of 1 points

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If the estimates of the coefficients of interest change substantially across

specifications,

Selected
Answer:
then this often provides evidence that the original specification
had omitted variable bias.

Answers: then you should change the scale of the variables to make the
changes appear to be smaller.

then this often provides evidence that the original specification

had omitted variable bias.

then this can be expected from sample variation.

then choose the specification for which your coefficient of

interest is most significant.

 Question 9
1 out of 1 points
Put the following steps of the model-building process in the order in which it
would be statistically most appropriate to do them:

(i) Estimate model

(ii) Conduct hypothesis tests on coefficients
(iii) Remove irrelevant variables
(iv) Conduct diagnostic tests on the model residuals

Selected
Answer:
(i) then (iv) then (iii) then (ii)

Answers: (i) then (ii) then (iii) then (iv)

(i) then (iv) then (iii) then (ii)

(i) then (iii) then (ii) then (iv).

(i) then (iv) then (ii) then (iii)

 Question 10
1 out of 1 points
If you reject the null hypothesis when performing a RESET test, what should you
conclude?

Selected

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Answer: the original model is incorrectly specified and can be improved

upon

Answers:
the original model is incorrectly specified and can be improved
upon

relevant variable are omitted and the coefficient estimates of

included variables are biased

an incorrect functional form was used

at least one of the original coefficients is not equal to zero

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Combined Quiz Bank - Combine answers for quizzes mid

semester and final exam
Economic and Financial Modelling (Western Sydney University)

Studocu is not sponsored or endorsed by any college or university

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1. Module 05 - Model Specification and Multicollinearity

 Question 1
1 out of 1 points
What is an interaction term?
Selected
Answer:
an additional variable that is the product of 2 other independent
variables
Answers:
an additional variable that is the product of 2 other independent
variables
the expected value formed by multiplying a variable by its
estimated coefficient
a variable indicating an observation may be in the dataset
multiple times
a variable indicating 2 observations are related

Response Correct!
Feedback:

 Question 2
1 out of 1 points
How do you interpret the estimated value of β1 in the following model?

Selected
Answer:
the elasticity of y with respect to x1

Answers: the slope of the line representing the relationship

between y and x1

the elasticity of y with respect to x1

cannot be determined without more information

the mean value of ln(y) when ln(x1) = 0.
Response Correct!
Feedback:

 Question 3
1 out of 1 points
You have estimated the simple regression model below.

What is the elasticity when x1 = 8.49?

Selected

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Answer: 2.10
Answers:
2.10
263.19
-24.70
311.39

Response Correct!
Feedback:

 Question 4
1 out of 1 points
If your regression results show a high R2, adj R2, and a significant F-test,
but low t values for the coefficients, what is the most likely cause?
Selected
Answer:
collinearity
Answers: irrelevant variables included

omitted relevant variables

heteroskedasiticity

collinearity
Response Correct!
Feedback:

 Question 5
1 out of 1 points
You estimate 4 different specifications of an econometric model by adding a variable each
time and get the following results

Model A 0.3458 0.3285 22.56

Model B 0.3689 0.3394 22.37
Model C 0.4256 0.3916 21.21
Model D 0.4299 0.3911 21.79

Which model appears to be correct specified?

Selected
Answer:
C
Answers: A

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C
D
Response Correct!
Feedback:

 Question 6
1 out of 1 points
Omitted variable bias is a problem because _______ .
Selected
Answer:
it prevents correctly estimating marginal effects.
Answers: it prevents the model from being able to be estimated by ordinary least
squares.

it prevents correctly estimating marginal effects.

it causes the model to no longer be linear in the parameters.

it causes perfect multicollinearity.

Response Correct!
Feedback:

 Question 7
1 out of 1 points
If you reject the null hypothesis when performing a RESET test, what
should you conclude?
Selected
Answer:
the original model is incorrectly specified and can be improved
upon
Answers: an incorrect functional form was used

relevant variable are omitted and the coefficient estimates of

included variables are biased
at least one of the original coefficients is not equal to zero

the original model is incorrectly specified and can be improved

upon
Response Correct!
Feedback:

 Question 8
1 out of 1 points

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You have estimated a model of two variables shown below.

If x1 decreases by 2 units, what is the expected change in y?

Selected
Answer:
y increases by 8 percent.

Answers:
y increases by 8 percent.

y decreases by 8
percent.

y decreases by .08 units.

y increases by 4 units.

Response Correct!
Feedback:

 Question 9
1 out of 1 points
How can you estimate non-linear function forms using least squares?
Selected
Answer:
transform, such as squaring or cubing, some explanatory
variables

Answers: estimate the linear approximation over small ranges at a time

It cannot be done. You need to use another estimation

technique.

transform, such as squaring or cubing, some explanatory

variables

use a very large sample so you do not have to assume the error
terms are normally distributed

Response Correct!
Feedback:

 Question 10
1 out of 1 points
Which of the following is true?
Selected
Answer:

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A functional form misspecification can occur if the level of a variable is

used when the logarithm is more appropriate.
Answers:
A functional form misspecification can occur if the level of a variable is
used when the logarithm is more appropriate.
A functional form misspecification does not lead to biasedness in the
ordinary least squares estimators.

A functional form misspecification occurs only if a key variable

is uncorrelated with the error term.

A functional form misspecification does not lead to inconsistency in the

ordinary least squares estimators.

Response Correct!
Feedback:

 Question 11
1 out of 1 points
How does including an irrelevant variable in a regression model affect the
estimated coefficient of other variables in the model?
Selected
Answer:
they are unbiased but have larger standard errors
Answers:
they are unbiased but have larger standard errors
they are biased downward and have smaller standard errors
they are biased and the bias can be negative or positive
they are biased upward and have larger standard errors
Response Correct!
Feedback:

 Question 1
1 out of 1 points
The interpretation of goodness-of-fit measures changes in the presence of heteroskedasticity.
Selected
Answer: Fals
e

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Answers: True

Fals
e
 Question 2
1 out of 1 points
Which of the following tests is used to compare the Ordinary Least Squares (OLS) estimates
and the Weighted Least Squares (WLS) estimates?
Selected
Answer: The Hausman test
Answers: The White test

The Hausman test

The Breusch-Godfrey test
The Durbin-Watson test
 Question 3
1 out of 1 points
If you run a LM test for heteroskedasiticity and reject the null
hypothesis, what should you conclude?

Selected
Answer:
at least one coefficients in the auxiliary regression is
significantly different from zero, the
assumption is unlikely to be true
Answers:
at least one coefficients in the auxiliary regression is
significantly different from zero, the
assumption is unlikely to be true
there is no evidence of heteroskedasticity, the
assumption is most likely true
there is heteroskedasticity present and it is correctly
specified as tested
there is heteroskedasticity, but it is not linear in the
explanatory variables
 Question 4
1 out of 1 points

The LM (Lagrange Multiplier) test generates a test statistic . To what

does the K in this distribution refer?
Selected
Answer: the number of explanatory variables in the
auxiliary regression
Answers: the number of explanatory variables in the initial
model

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the number of explanatory variables in the

auxiliary regression
the statistical significance level chosen for the LM
test

N-K—the degrees of freedom in econometric

model of interest

 Question 5
1 out of 1 points
Multicollinearity among the independent variables in a linear regression model causes the
heteroskedasticity-robust standard errors to be large.
Selected
Answer: True
Answers:
True
Fals
e
 Question 6
1 out of 1 points
What are the consequences of using least squares when heteroskedasticity
is present?
Selected
Answer: all coefficient estimates are biased for variables correlated
with the error term
Answers: no consequences, coefficient estimates are still unbiased
it requires very large sample sizes to get efficient estimates

all coefficient estimates are biased for variables correlated

with the error term
confidence intervals and hypothesis testing are inaccurate due to
inflated standard errors
 Question 7
1 out of 1 points
What is the tradeoff researchers face when deciding how to deal with
heteroskedasticity?

Selected
Answer:
White’s robust estimator requires no assumptions about the
structure of the variance, but it is not as efficient as GLS
estimates when the right structure is imposed on the variance
Answers: GLS gives minimum variance, but results are more difficult to
interpret

White’s robust estimator requires no assumptions about the

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structure of the variance, but it is not as efficient as GLS

estimates when the right structure is imposed on the variance
White’s robust estimator should be used for hypothesis testing,
but GLS is better for interval estimation
Goldfeld-Quandt overstates heteroskedasticity but LM leads to
more Type I errors

 Question 8
1 out of 1 points
When using WLS to correct for heteroskedasticity, what weight should be used?
Selected
Answer: whatever weight scales all variables and creates a homoskedastic error
variance
Answers:
the inverse of the error variance at
whatever weight is determined by the Goldfeld-Quandt test

whatever weight scales all variables and creates a homoskedastic error

variance
the residuals from the initial regression model
 Question 9
1 out of 1 points
How are coefficient estimates from WLS (weighted least squares)
interpreted?

Selected
Answer:
there is no difference in interpretation since each observation is
scaled by the same divisor
Answers: They should only be used for hypothesis testing. Coefficient
estimates from the un-weighted, original model should be used
for prediction.
take the inverse of the natural logarithm of the coefficient to
find marginal effects

there is no difference in interpretation since each observation is

scaled by the same divisor
they must be scaled up by the weight used in order to calculate
marginal effects

 Question 10
1 out of 1 points

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The scatterplots above show the estimated residuals plotted against predicted
values of the dependent variable. In which model is WLS LEAST likely to
be an effective solution for the heteroskedasticity?
Selected
Answer: Model B
Answers: Model A

Model B
Model C
Model D
 Question 11
1 out of 1 points
How should you estimate a model with heteroskedasticity when you
are confident the error variance is a function of one continuous
variable?
Selected
Answer:
WLS or GLS
Answers:
WLS or GLS
White Robust
Quasi-Least Squares
FGLS
 Question 12
1 out of 1 points
Consider the following regression equation: . Which of the following
indicates a functional form misspecification in E(y|x)?
Selected
Answer: Ordinary Least Squares estimates equal Weighted Least Squares estimates.
Answers: Ordinary Least Squares estimates exceed Weighted Least Squares estimates
by a small magnitude.
Weighted Least Squares estimates exceed Ordinary Least Squares estimates
by a small magnitude.

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Ordinary Least Squares estimates equal Weighted Least Squares estimates.

Ordinary Least Square estimates are positive while Weighted Least
Squares estimates are negative.

 Question 13
1 out of 1 points
Heteroskedasticity is a violation of which assumption of the multiple
regression model?
Selected
Answer:

Answers: The values of each xk are not random and are not exact
linear functions of the other explanatory variables

 Question 14
1 out of 1 points
If you model has heteroskedastic error terms, but you do not know
the functional form of the variance equation, what should be done?
Selected
Answer:
use White’s Robust Estimator or heteroskedasticity-robust
estimators

Answers: use weighted least squares

use White’s Robust Estimator or heteroskedasticity-robust

estimators

add observations to the dataset and estimate again

try different functional forms for the variance until the
Lagrange Multiplier falls 10%
 Question 15
1 out of 1 points
If you have heteroskedasticity such that the sample can be divided into groups with each
group having a different error variance, what estimation technique should be used?
Selected
Answer: FGLS—feasible generalized least squares
Answers: WLS—Weighted least squares
White’s robust estimator
log-linear least squares

FGLS—feasible generalized least squares

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 Question 16
1 out of 1 points

The LM (Lagrange Multiplier) test generates a test statistic

Where is the R in the test statistic measured?
2

Selected
Answer: the auxiliary regression of residuals as a function of the
explanatory variables generating the heteroskedasticity
Answers: the original econometric model before any test of
heteroskedasticity has been performed
the original econometric model when estimated using the
White correction technique
the average from all the auxiliary regressions estimated with
each explanatory variable as a function of the other
explanatory variables

the auxiliary regression of residuals as a function of the

explanatory variables generating the heteroskedasticity

 Question 1
1 out of 1 points

Which of the following is a DISADVANTAGE of using pure time-series models

(relative to structural models)?

Selected
Answer:
They are not theoretically
motivated
Correct
Answer:
They are not theoretically
motivated
 Question 2
1 out of 1 points
When using confidence intervals and hypothesis tests with time series data, when
the equation errors, , are correlated the coefficient estimates are

Selected
Answer:
unbiased, but not
efficient
Correct
Answer:
unbiased, but not
efficient
 Question 3
1 out of 1 points
Autocorrelation refers to a situation in which

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Selected
Answer: successive error terms derived from the application of regression analysis to
time series data are correlated.
Correct
Answer: successive error terms derived from the application of regression analysis to
time series data are correlated.
 Question 4
1 out of 1 points
The Durbin-Watson statistic is used to test for

Selected
Answer: autocorrelation.

Correct
Answer: autocorrelation.

 Question 5
1 out of 1 points
Which of the following assumptions are required to show the consistency,
unbiasedness and efficiency of the OLS estimator?

i) E(ut) = 0
ii) Var(ut) = σ2
iii) Cov(ut, ut-j) = 0
iv) ut~N(0, σ2)

Selected
Answer:
(i), (ii), and (iii)
only
Correct
Answer:
(i), (ii), and (iii)
only
 Question 6
1 out of 1 points
Suppose that the Durbin Watson test is applied to a regression containing two
explanatory variables plus a constant (e.g. equation 2 above) with 50 data points.
The test statistic takes a value of 1.53. What is the appropriate conclusion?

Selected
Answer:
The test result is
inconclusive
Correct
Answer:
The test result is

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inconclusive
 Question 7
1 out of 1 points
If a Durbin Watson statistic takes a value close to zero, what will be the value of
the first order autocorrelation coefficient?

Selected
Answer:
Close to plus
one
Correct
Answer:
Close to plus
one
 Question 8
1 out of 1 points
Which of the following is a typical characteristic of financial asset return time-
series?

Selected
Answer:
They are highly
autocorrelated
Correct
Answer:
They are highly
autocorrelated
 Question 9
1 out of 1 points
Which of the following may be consequences of one or more of the CLRM
assumptions being violated?

i) The coefficient estimates are not optimal

ii) The standard error estimates are not optimal

iii) The distributions assumed for the test statistics are inappropriate

iv) Conclusions regarding the strength of relationships between the dependent

Selected
Answer:
(i), (ii), (iii), and
(iv)
Correct
Answer:
(i), (ii), (iii), and
(iv)
 Question 10
1 out of 1 points
Autocorrelation may be the result of

Selected
Answer:
All of the above are

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correct.

Correct
Answer:
All of the above are
correct.

 Question 1
0 out of 1 points
How do you check for cointegration of two series?

Selected
Answer:
subract one series from the other and check of stationarity of the
difference
Answers: subract one series from the other and check of stationarity of the
difference
estimate a regression of one as a function of the other and test the
significance of the parameter estimates

test the significance of the covariance between the two series

estimate a regression of one series as fuction of the other, then perform

an augmented Dickey-Fuller test on estimated residuals

 Question 2
0 out of 1 points
Which assumption is most likely to be violated with times series data:

 Question 3

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1 out of 1 points

The model yt = yt – 1 + et, t = 1, 2, … represents a(n):

Selected
Answer: random walk process
Answers:
random walk process
AR(2) process
I(1) process
A random walk with
drift process
 Question 4
1 out of 1 points
Consider the following two ways of expressing the Dickey-Fuller test regression:

 Question 5
1 out of 1 points

If a process is said to be integrated of order one, or I(1):

Selected
Answer: the first difference of the process is weakly dependent.

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Answers:
the first difference of the process is weakly dependent.

averages of such processes already satisfy the standard limit theorems.

it is stationary at level.

it does not have a unit root.

 Question 6
0 out of 1 points
Which of the following is a common way to convert a nonstationary series to a stationary series?
Selected
Answer:
running a spurious regression
Answers: running a spurious regression

first differencing
estimating distributed lags
cointegrating
 Question 7
0 out of 1 points
Which of the following statements is true of spurious regressions?

Selected
Answer: The OLS estimates of the population parameters are efficient and unbiased
and the t statistic is valid.

Answers: The OLS estimates of the population parameters are efficient and unbiased
and the t statistic is valid.

Spurious regressions are limited to I(0) processes, and are not possible in case
of I(1) processes.

Spurious regressions are limited to I(1) processes, and are not possible in case
of I(0) processes.

Even if the explanatory variables and the dependent variable are independent
times series processes, the R2 can large.

 Question 8

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1 out of 1 points
Which of the following are characteristics of a stationary process?

i) It crosses its mean value frequently

ii) It has constant mean and variance
iii) It contains no trend component
iv) It will be stationary in first difference form

Selected
Answer: (i), (ii), (iii), and
(iv)
Answers: (ii) and (iv) only

(i), (ii), (iii), and

(iv)
(i) and (iii) only
(i), (ii), and (iii)
only
 Question 9
0 out of 1 points
A trend stationary process:

Selected
Answer: is I(0) about its trend.

Answers: is I(0) about its trend.

has a linear trend in its mean.

can be mistaken for unit root process.

all of the options given.

 Question 10
1 out of 1 points
What is the difference between the Dickey-Fuller Tests 1, 2, and 3?
Selected
Answer: they test for stationarity around zero, stationarity around a constant, and stationarity
around a trend line, respectively
Answers:
they test for stationarity around zero, stationarity around a constant, and stationarity
around a trend line, respectively
they test ρ <1, ρ>1, and ρ=1, respectively
they use t, τ, and F tests, respectively
they test for integration of orders 1, 2 and 3 respectively.

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Review Quiz answers

 Question 1
1 out of 1 points
Which of the following is a difference between panel and pooled cross-
sectional data?
Selected
Answer:
A panel data set consists of data on the same cross-sectional
units over a given period of time while a pooled data set consists
of data on different cross-sectional units over a given period of
time
Answers: A panel data set consists of data on different cross-sectional
units over a given period of time while a pooled data set consists
of data on the same cross-sectional units over a given period of
time

A panel data set consists of data on the same cross-sectional

units over a given period of time while a pooled data set consists
of data on different cross-sectional units over a given period of
time
A panel data consists of data on a single variable measured at a
given point in time while a pooled data set consists of data on the
same cross-sectional units over a given period of time
A panel data set consists of data on a single variable measured
at a given point in time while a pooled data set consists of data
on more than one variable at a given point in time
 Question 2
1 out of 1 points
Which of the following is a statistic that can be used to test
hypotheses about a single population parameter?
Selected
Answer:
t statistic
Answers:
t statistic
Chi-square statistic
F statistic
Durbin Watson
statistic
 Question 3
1 out of 1 points

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The parameters of an econometric model _____

Selected
Answer:
describe the strength of the relationship between the variable
under study and the factors affecting it
Answers: include all unobserved factors affecting the variable being
studied

describe the strength of the relationship between the variable

under study and the factors affecting it
refer to the explanatory variables included in the model
refer to the predictions that can be made using the model
 Question 4
1 out of 1 points
A dependent variable is also known as a(n) _____

Selected
Answer: response
variable
Answers:
response
variable
explanatory
variable
control variable
predictor
variable
 Question 5
0 out of 1 points
Suppose the variable x2 has been omitted from the following regression
equation, y = β0+ β1x1 + β2x2 + u.
is the estimator obtained when x2 is omitted from the equation.The bias

in is positive if _____.

Selected
Answer: β2 < 0 and x1 and x2 are positively
correlated
Answers: β2 < 0 and x1 and x2 are positively
correlated

β2 > 0 and x1 and x2 are positively

correlated
β2 < 0 and x1 and x2 are negatively
correlated

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β2 = 0 and x1 and x2 are negatively

correlated
 Question 6
0 out of 1 points
Which of the following statements is true?
Selected
Answer:
Standard error of an estimate does not affect the
confidence interval for the estimate.
Answers: Standard error of an estimate does not affect the
confidence interval for the estimate.

The upper bound of the confidence interval for a regression

coefficient, say βj, is given by:

When the standard error of an estimate

increases, the confidence interval for the
estimate narrows down.
The lower bound of the confidence interval for a regression
coefficient, say βj, is given by:

 Question 7
0 out of 1 points
In the equation below, β0 is the _____

Selected
Answer: dependent
variable
Answers: dependent
variable
independent
variable

intercept
parameter
slope parameter
 Question 8
0 out of 1 points
Which of the following models is used quite often to capture
decreasing or increasing marginal effects of a variable?
Selected

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Answer: Models with logarithmic

functions
Answers: Models with logarithmic
functions
Models with interaction
terms

Models with quadratic

functions
Models with variables in
level
 Question 9
0 out of 1 points
Suppose the variable x2 has been omitted from the following regression
equation, y=β0 + β1x1 + β2x2 + u. β1 is the estimator obtained when x2 is
omitted from the equation.If E((β1 ) ̃ ) >β1, (β1 ) ̃ is said to _____.

Selected
Answer: be biased toward
zero
Answers: be biased toward
zero

have an upward
bias
have a downward
bias
be unbiased
 Question 10
1 out of 1 points
Which of the following tools is used to test multiple linear
restrictions?
Selected
Answer:
F test
Answers:
F test
z test
t test
Unit root
test
 Question 11

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1 out of 1 points
Which of the following is true?
Selected
Answer:
A functional form misspecification can occur if the
level of a variable is used when the logarithm is
more appropriate.
Answers:
A functional form misspecification can occur if the
level of a variable is used when the logarithm is
more appropriate.
A functional form misspecification does not lead
to biasedness in the ordinary least squares
estimators.
A functional form misspecification occurs only if a
key variable is uncorrelated with the error term.
A functional form misspecification does not lead
to inconsistency in the ordinary least squares
estimators.
 Question 12
0 out of 1 points
Which of the following refers to panel data?
Selected
Answer:
Data on the unemployment rate in a country over a 5-year
period
Answers: Data on the unemployment rate in a country over a 5-year
period

"Data on the birth rate, death rate and population growth rate in
developing countries over a 10-year period"
Data on the income of 5 members of a family on a particular
year
Data on the price of a company s share during a year
 Question 13
0 out of 1 points
If the total sum of squares (SST) in a regression equation is 81, and the residual
sum of squares (SSR) is 25, what is the explained sum of squares (SSE)?

Selected
Answer: 64
Answers: 64
18

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32

56
 Question 14
0 out of 1 points
Which of the following correctly defines F statistic if SSE R represents sum of squared
residuals from the restricted model of hypothesis testing, SSE U represents sum of squared
residuals of the unrestricted model, and J is the number of restrictions placed? Both
models do not have an intercept.
Selected
Answer:

Answers:

 Question 15
1 out of 1 points
Changing the unit of measurement of any independent
variable, where log of the dependent variable appears in
the regression:
Selected
Answer:
affects only the intercept coefficient.
Answers:
affects only the intercept coefficient.
affects only the slope coefficient.
affects both the slope and intercept
coefficients.
affects neither the slope nor the intercept
coefficient.
 Question 16
0 out of 1 points
In the following equation, gdp refers to gross domestic
product, and FDI refers to foreign direct investment.

log(gdp) = 2.65 + 0.527log(bankcredit) + 0.222FDI

(0.13) (0.022) (0.017)

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Which of the following statements is then true?

Selected
Answer:
If FDI increases by 1%, gdp increases by
approximately 22.2%, the amount of bank credit
remaining constant.
Answers: If FDI increases by 1%, gdp increases by
approximately 22.2%, the amount of bank credit
remaining constant.
If FDI increases by 1%, gdp increases by
approximately 26.5%, the amount of bank credit
remaining constant.

If FDI increases by 1%, gdp increases by

approximately 24.8%, the amount of bank credit
remaining constant.
If FDI increases by 1%, gdp increases by
approximately 52.7%, the amount of bank credit
remaining constant.
 Question 17
0 out of 1 points
Which of the following statements is true?
Selected
Answer:
All estimators in a regression model that are
consistent are also unbiased.
Answers: All estimators in a regression model that are
consistent are also unbiased.

In time series regressions, OLS estimators are

always unbiased.
Almost all economists agree that unbiasedness is
a minimal requirement for an estimator in
regression analysis.
The standard error of a regression, , is not an unbiased estimator
for , the standard deviation of the error, u, in a multiple regression
model.
 Question 18
0 out of 1 points
A measurement error occurs in a regression model when
_____.

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Selected
Answer:
the model includes more than two independent
variables
Answers: the model includes more than two independent
variables

the observed value of a variable used in the

model differs from its actual value
the partial effect of an independent variable
depends on unobserved factors
the dependent variable is binary
 Question 19
1 out of 1 points
In the equation below, what is the estimated value of β 0?

Selected
Answer:

Answers:

 Question 20
1 out of 1 points
If δ1 = Cov(x1/x2) / Var(x1) where x1 and x2 are two independent variables in a
regression equation, which of the following statements is true?
Selected
Answer: If x has a positive partial effect on the dependent variable, and δ > 0, then
2 1

the inconsistency in the simple regression slope estimator associated with

x is positive.
1

Answers:
If x has a positive partial effect on the dependent variable, and δ > 0, then
2 1

the inconsistency in the simple regression slope estimator associated with

x is positive.
1

If x1 has a positive partial effect on the dependent variable, and δ1 > 0,

then the inconsistency in the simple regression slope estimator

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associated with x1 is positive.

If x1 has a positive partial effect on the dependent variable, and δ1 > 0,
then the inconsistency in the simple regression slope estimator
associated with x1 is negative.
If x2 has a positive partial effect on the dependent variable, and δ1 > 0,
then the inconsistency in the simple regression slope estimator
associated with x1 is negative.
 Question 21
0 out of 1 points
Which of the following is the first step in empirical economic analysis?
Selected
Answer:
Collection of data
Answers: Collection of data
Statement of hypotheses

Specification of an econometric model

Testing of hypotheses
 Question 22
0 out of 1 points
If j , an unbiased estimator of j , is consistent, then the:
Selected
Answer:
distribution of j remains unaffected as the sample size grows.
Answers:
distribution of j remains unaffected as the sample size grows.

distribution of j tends toward a standard normal distribution as the

sample size grows.

distribution of j becomes more and more loosely distributed

around j as the sample size grows.

distribution of j becomes more and more tightly distributed

around j as the sample size grows.
 Question 23
0 out of 1 points
Consider the following regression model below. Which of the following is a
property of Ordinary Least Square (OLS) estimates of this model and their
associated statistics?

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Selected
Answer: The sum, and therefore the sample average of the OLS
residuals, is positive
Answers: The sum, and therefore the sample average of the OLS
residuals, is positive
The sum of the OLS residuals is negative
The sample covariance between the regressors and the OLS
residuals is positive

The point (x-bar, y-bar) always lies on the OLS regression line"
 Question 24
1 out of 1 points
The term _____ refers to the problem of small sample size.
Selected
Answer:
micronumerosit
y
Answers:
micronumerosit
y
multicollinearit
y
homoskedastici
ty
heteroskedasti
city
 Question 25
0 out of 1 points
Which of the following correctly represents the equation for
adjusted R2?
Selected
Answer:
2
= 1 – [SSR]/[SST/(n – 1)]

Answers: 2
= 1 – [SSR]/[SST/(n – 1)]

2
= 1 – [SSR/(n –k – 1)]/[SST/(n+1)]

2
= 1 – [SSR/(n –1)]/[SST/(n+1)]

2
= 1 – [SSR/(n –k – 1)]/[SST/(n –

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1)]

 Question 26
0 out of 1 points
A proxy variable _____.
Selected
Answer:
is detected by running the Davidson-MacKinnon
test
Answers: is detected by running the Davidson-MacKinnon
test
cannot contain binary information
increases the error variance of a regression
model

is used when data on a key independent

variable is unavailable
 Question 27
0 out of 1 points
A change in the unit of measurement of the dependent
variable in a model does not lead to a change in:
Selected
Answer:
the standard error of the regression.
Answers: the standard error of the regression.
the sum of squared residuals of the
regression.

the goodness-of-fit of the regression.

the confidence intervals of the
regression.
 Question 28
1 out of 1 points
Which of the following is a nonlinear regression model?

Selected
Answer:

Answers:

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 Question 29
0 out of 1 points
Which of the following statements is true under the Gauss-
Markov assumptions?
Selected
Answer:
Among a certain class of estimators, OLS
estimators are best linear unbiased, but are
asymptotically inefficient.
Answers: Among a certain class of estimators, OLS
estimators are best linear unbiased, but are
asymptotically inefficient.
Among a certain class of estimators, OLS
estimators are biased but asymptotically efficient.

Among a certain class of estimators, OLS

estimators are best linear unbiased and
asymptotically efficient.
None of the above
 Question 30
0 out of 1 points
Which of the following correctly identifies an advantage of
using adjusted R2 over R2?
Selected
Answer:
Adjusted R2 corrects the bias in R2.
Answers: Adjusted R2 corrects the bias in R2.

The penalty of adding new independent variables

is better understood through adjusted R2 than R2.
The adjusted R2 can be calculated for models
having logarithmic functions while R2 cannot be
calculated for such models.
Adjusted R2 is easier to calculate than R2.
 Question 31
0 out of 1 points

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What does the equation denote if the regression equation is ?

Selected
Answer: The total sum of squares
Answers: The total sum of squares
The population regression
function
The explained sum of squares

The sample regression

function
 Question 32
0 out of 1 points
Which of the following statements is true?
Selected
Answer:
The F statistic is also referred to as
the score statistic.
Answers:
The F statistic is also referred to as
the score statistic.
Even if the error terms in a regression equation, u1,
u2,….., un, are not normally distributed, the
estimated coefficients can be normally distributed.

A normally distributed random variable is

symmetrically distributed about its mean, it can
take on any positive or negative value (but with
zero probability), and more than 95% of the area
under the distribution is within two standard
deviations.
If variance of an independent variable in a regression
model, say x1, is greater than 0, or Var(x1) > 0, the
inconsistency in 1 (estimator associated with x1) is
negative, if x1 and the error term are positively related.
 Question 33
0 out of 1 points
Residual analysis refers to the process of:
Selected
Answer:
sampling and collection of data in such a way to
minimize the squared sum of residuals.

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Answers: sampling and collection of data in such a way to

minimize the squared sum of residuals.

examining individual observations to see whether

the actual value of a dependent variable differs
from the predicted value.
transforming models with variables in level to
logarithmic functions so as to understand the
effect of percentage changes in the independent
variable on the dependent variable.
calculating the squared sum of residuals to draw
inferences for the consistency of estimates.
 Question 34
0 out of 1 points
Which of the following is true?
Selected
Answer:
A variable has a causal effect on another variable if both
variables increase or decrease simultaneously
Answers: A variable has a causal effect on another variable if both
variables increase or decrease simultaneously

The notion of ceteris paribus plays an important role in causal

analysis
Difficulty in inferring causality disappears when studying data at
fairly high levels of aggregation
The problem of inferring causality arises if experimental data is
used for analysis
 Question 35
0 out of 1 points
The error term in a regression equation is said to exhibit homoskedasticty if _____

Selected
Answer: it has the same value for all values of the explanatory variable
Answers: it has the same value for all values of the explanatory variable

it has the same variance for all values of the explanatory

variable
it has zero conditional mean
if the error term has a value of one given any value of the
explanatory variable
 Question 36
1 out of 1 points

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If OLS estimators satisfy asymptotic normality, it implies

that:
Selected
Answer:
they are approximately normally distributed in
large enough sample sizes.
Answers:
they are approximately normally distributed in
large enough sample sizes.
they are approximately normally distributed in
samples with less than 10 observations.
they have a constant mean equal to zero and
variance equal to σ2.
they have a constant mean equal to one and
variance equal to σ.
 Question 37
0 out of 1 points
Which of the following is true of R2?
Selected
Answer: R2 is also called the standard error of regression.
Answers: R2 is also called the standard error of regression.
A low R2 indicates that the Ordinary Least Squares line fits the
data well.
R2 usually decreases with an increase in the number of
independent variables in a regression.

R2 shows what percentage of the total variation in the dependent

variable, Y, is explained by the explanatory variables.
 Question 38
1 out of 1 points
High (but not perfect) correlation between two or more
independent variables is called _____.
Selected
Answer:
multicollinearit
y
Answers:
multicollinearit
y
heteroskedasti
cty
micronumerosi

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ty
homoskedastic
ty
 Question 39
0 out of 1 points
Econometrics is the branch of economics that _____
Selected
Answer:
studies the behavior of individual economic agents in making
economic decisions
Answers: studies the behavior of individual economic agents in making
economic decisions

develops and uses statistical methods for estimating economic

relationships
"deals with the performance, structure, behavior, and decision-
making of an economy as a whole"
applies mathematical methods to represent economic theories
and solve economic problems
 Question 40
0 out of 1 points
Two equations form a nonnested model when:
Selected
Answer:
each equation has the same independent
variables.
Answers: each equation has the same independent
variables.
one is logarithmic and the other is quadratic.
there is only one independent variable in both
equations.

neither equation is a special case of the other.

 Question 41
0 out of 1 points
In the regression of y on x, the error term exhibits heteroskedasticity if _____

Selected
Answer: it has a constant
variance
Answers: it has a constant
variance

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Var(y|x) is a function
of x
x is a function of y
y is a function of x
 Question 42
0 out of 1 points
The general t statistic can be written as:
Selected
Answer:
t = (estimate – hypothesized value) /
variance
Answers: t = (estimate – hypothesized value) /
variance

t = (estimate – hypothesized
value) / standard error
t = hypothesized value / standard error
t = estimate –hypothesized value
 Question 43
0 out of 1 points
Which of the following correctly identifies a reason why
some authors prefer to report the standard errors rather
than the t statistic?
Selected
Answer:
Standard errors can be used directly to test
multiple linear regressions.
Answers: Standard errors can be used directly to test
multiple linear regressions.
The F statistic can be reported just by looking at
the standard errors.
Standard errors are always positive.

Having standard errors makes it easier to

compute confidence intervals.
 Question 44
0 out of 1 points
Which of the following correctly identifies a limitation of
logarithmic transformation of variables?
Selected
Answer:

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Taking log of a variable often expands its range

which can cause inefficient estimates.
Answers: Taking log of a variable often expands its range
which can cause inefficient estimates.
Logarithmic transformations of variables are likely
to lead to heteroskedasticity.

Logarithmic transformations cannot be used if a

variable takes on zero or negative values.
Taking log of variables make OLS estimates more
sensitive to extreme values in comparison to
variables taken in level.
 Question 45
0 out of 1 points
If j is an OLS estimator of a regression coefficient associated with one of the
explanatory variables, such that j= 1, 2, …., n, asymptotic standard error of j will
refer to the:
Selected
Answer:
estimated variance of j when the error term is normally
distributed.
Answers:
estimated variance of j when the error term is normally
distributed.
estimated variance of a given coefficient when
the error term is not normally distributed.
square root of the estimated variance of j when the
error term is normally distributed.

square root of the estimated variance of j when the

error term is not normally distributed.
 Question 46
1 out of 1 points
Which of the following is true of experimental data?
Selected
Answer:
Experimental data are collected in laboratory environments in
the natural sciences
Answers:
Experimental data are collected in laboratory environments in
the natural sciences

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Experimental data cannot be collected in a controlled

environment
Experimental data is sometimes called observational data
Experimental data is sometimes called retrospective data
 Question 47
0 out of 1 points
Suppose the variable x2 has been omitted from the following regression
equation, y = β0 + β1x1 + β2x2 + u.
is the estimator obtained when x2 is omitted from the equation.The bias

in is negative if _____.

Selected
Answer: β2 > 0 and x1 and x2 are positively
correlated
Answers: β2 > 0 and x1 and x2 are positively
correlated
β2 = 0 and x1 and x2 are negatively
correlated
β2 = 0 and x1 and x2 are negatively
correlated

β2 < 0 and x1 and x2 are positively

correlated
 Question 48
0 out of 1 points
A data set that consists of observations on a variable or several variables
over time is called a _____ data set
Selected
Answer:
binary
Answers: binary
cross-sectional

time series
experimental
 Question 49
0 out of 1 points
Which of the following is true of measurement error?
Selected
Answer:
If measurement error in a dependent variable has
zero mean, the ordinary least squares estimators
for the intercept are biased and inconsistent.

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Answers: If measurement error in a dependent variable has

zero mean, the ordinary least squares estimators
for the intercept are biased and inconsistent.

If measurement error in an independent variable is

uncorrelated with the variable, the ordinary least
squares estimators are unbiased.
If measurement error in an independent variable is
uncorrelated with other independent variables, all
estimators are biased.
If measurement error in a dependent variable is
correlated with the independent variables, the
ordinary least squares estimators are unbiased.
 Question 50
0 out of 1 points
The normality assumption implies that:
Selected
Answer:
the population error u is dependent on the
explanatory variables and is normally distributed
with mean equal to one and variance σ2.
Answers: the population error u is dependent on the
explanatory variables and is normally distributed
with mean equal to one and variance σ2.

the population error u is independent of the

explanatory variables and is normally distributed
with mean zero and variance σ2.
the population error u is dependent on the
explanatory variables and is normally distributed
with mean zero and variance σ.
the population error u is independent of the
explanatory variables and is normally distributed
with mean equal to one and variance σ.
 Question 51
0 out of 1 points
Which of the following statements is true of hypothesis
testing?
Selected
Answer:
OLS estimates maximize the sum of squared
residuals.

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Answers: The t test can be used to test multiple linear

restrictions.
OLS estimates maximize the sum of squared
residuals.
A test of single restriction is also referred to as a
joint hypotheses test.

A restricted model will always have fewer

parameters than its unrestricted model.
 Question 52
0 out of 1 points
Data on the income of law graduates collected at different times during the
same year is_____
Selected
Answer:
time series data
Answers: panel data
experimental data
time series data

cross-sectional data
 Question 53
0 out of 1 points
Which of the following is an example of time series data?
Selected
Answer:
Data on the unemployment rates in different parts of a country
during a year
Answers: Data on the unemployment rates in different parts of a country
during a year
Data on the consumption of wheat by 200 households during a
year

Data on the gross domestic product of a country over a period

of 10 years
Data on the number of vacancies in various departments of an
organization on a particular month
 Question 54
0 out of 1 points
The residual sum of squares for the regression function below is defined as _____

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Selected
Answer:

Answers:

 Question 55
0 out of 1 points
If an independent variable in a multiple linear regression
model is an exact linear combination of other independent
variables, the model suffers from the problem of _____.
Selected
Answer:
homoskedasticity
Answers: homoskedasticity
heteroskedasticty

perfect
collinearity
omitted variable
bias
 Question 56
0 out of 1 points
In a multiple regression model, the OLS estimator is
consistent if:
Selected
Answer:
the sample size is less than the number of
parameters in the model.
Answers: the sample size is less than the number of
parameters in the model.
there is a perfect correlation between the
dependent variables and the error term.
there is no correlation between the dependent
variables and the error term.

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there is no correlation between the independent

variables and the error term.
 Question 57
0 out of 1 points
In the equation, y= β0 + β1x1 + β2x2 + u, β2 is a(n) _____.
Selected
Answer:
intercept
parameter
Answers: intercept
parameter
dependent
variable

slope parameter
independent
variable
 Question 58
0 out of 1 points
An empirical analysis relies on _____to test a theory
Selected
Answer:
common sense
Answers: common sense
ethical considerations

data
customs and conventions
 Question 59
0 out of 1 points
A useful rule of thumb is that standard errors are expected
to shrink at a rate that is the inverse of the:
Selected
Answer:
square of the sample size.
Answers: square of the sample size.
product of the sample size and the number of
parameters in the model.

square root of the sample size.

sum of the sample size and the number of

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parameters in the model.

 Question 60
0 out of 1 points
The value of R2 always _____.
Selected
Answer:
lies below 0
Answers: lies below 0
lies above 1

lies between 0 and 1

lies between 1 and 1.5
 Question 61
0 out of 1 points
If the explained sum of squares is 35 and the total sum of
squares is 49, what is the residual sum of squares?
Selected
Answer:
1
2
Answers: 1
2
1
8

1
4
1
0
 Question 62
0 out of 1 points
2 2
If R u = 0.6873, R R = 0.5377, number of restrictions (J) = 3, and N - K = 229, F
statistic equals:
Selected
Answer:
42.
1
Answers: 42.
1
21.
2

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28.
6

36.
5
 Question 63
0 out of 1 points
A predicted value of a dependent variable:
Selected
Answer:
is always equal to the actual value of the
dependent variable.
Answers: is always equal to the actual value of the
dependent variable.
represents the difference between the expected
value of the dependent variable and its actual
value.
is independent of explanatory variables and can
be estimated on the basis of the residual error
term only.

represents the expected value of the dependent

variable given particular values for the
explanatory variables.
 Question 64
0 out of 1 points
Which of the following is true of Regression Specification Error Test (RESET)?

Selected
Answer:
It helps in the detection of heteroskedasticity
when the functional form of the model is correctly
specified.
Answers: It helps in the detection of heteroskedasticity
when the functional form of the model is correctly
specified.

It tests if the functional form of a regression

model is misspecified.
It detects the presence of dummy variables in a
regression model.

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It helps in the detection of multicollinearity

among the independent variables in a regression
model.
 Question 65
0 out of 1 points
Exclusion of a relevant variable from a multiple linear
regression model leads to the problem of _____.
Selected
Answer:
perfect collinearity
Answers: perfect collinearity

misspecification of the
model
homoskedasticity
multicollinearity
 Question 66
0 out of 1 points
If the error term is correlated with any of the independent
variables, the OLS estimators are:
Selected
Answer:
biased and consistent.
Answers: biased and consistent.
unbiased and
inconsistent.

biased and
inconsistent.
unbiased and
consistent.
 Question 67
0 out of 1 points
Which of the following statements is true of confidence
intervals?
Selected
Answer:
Confidence intervals in a CLM do not depend on
the degrees of freedom of a distribution.
Answers: Confidence intervals in a CLM do not depend on
the degrees of freedom of a distribution.

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Confidence intervals in a CLM are also referred to

as point estimates.
Confidence intervals in a CLM can be truly
estimated when heteroskedasticity is present.

Confidence intervals in a CLM provide a range of

likely values for the population parameter.
 Question 68
0 out of 1 points
In the equation below, c denotes consumption and i denotes income. What is the
residual for the 5th observation if =$500 and =$475?

Selected
Answer: 97
5
Answers: 97
5
30
0

25
50
 Question 69
1 out of 1 points
Which of the following statements is true?
Selected
Answer:
Taking a log of a nonnormal distribution yields a
distribution that is closer to normal.
Answers:
Taking a log of a nonnormal distribution yields a
distribution that is closer to normal.
The mean of a nonnormal distribution is 0 and
the variance is σ2.
OLS estimators have the highest variance among
unbiased estimators.
The CLT assumes that the dependent variable is
unaffected by unobserved factors.

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 Question 70
0 out of 1 points
The assumption that there are no exact linear relationships
among the independent variables in a multiple linear
regression model fails if _____, where n is the sample size
and k is the number of parameters.
Selected
Answer:
n>2
Answers: n>2
n>k
n=k
+1

n<
k+1
 Question 71
1 out of 1 points
If the residual sum of squares (SSR) in a regression analysis is 66 and the total
sum of squares (SST) is equal to 90, what is the value of the coefficient of
determination?

Selected
Answer: 0.2
7
Answers:
0.2
7
1.2
0.5
5
0.7
3
 Question 72
0 out of 1 points
Which of the following statements is true?
Selected
Answer:
To make predictions of logarithmic dependent
variables, they first have to be converted to their
level forms.
Answers: To make predictions of logarithmic dependent
variables, they first have to be converted to their
level forms.

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If a new independent variable is added to a

regression equation, the adjusted R2 increases only
if the absolute value of the t statistic of the new
variable is greater than one.
F statistic can be used to test nonnested models.
 Question 73
0 out of 1 points
If a change in variable x causes a change in variable y, variable x is called the
_____

Selected
Answer: explained
variable
Answers: explained
variable

explanatory
variable
dependent
variable
response
variable
 Question 74
0 out of 1 points
A regression model suffers from functional form misspecification if _____.
Selected
Answer:
a key variable is binary.
Answers: a key variable is binary.
the dependent variable is binary.

an interaction term is omitted.

the coefficient of a key variable is zero.
 Question 75
1 out of 1 points
_____ has a causal effect on _____
Selected
Answer:
Income; consumption
Answers: Income; unemployment
Height; health

Income; consumption

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Age; wage
 Question 76
1 out of 1 points
"A data set that consists of a sample of individuals, households, firms, cities,
states, countries, or a variety of other units, taken at a given point in time, is
called a(n) _____"
Selected
Answer:
cross-sectional data set
Answers:
cross-sectional data set
longitudinal data set
time series data set
experimental data set
 Question 77
0 out of 1 points
Consider the following regression
equation: y = β1 + β2x1 + β3x2 + u. What does β1 imply?
Selected
Answer:
β1 measures the ceteris paribus effect
of x1 on u.
Answers: β1 measures the ceteris paribus effect
of x1 on u.

β1 measures the ceteris paribus effect

of x1 on y.
β1 measures the ceteris paribus effect
of y on x1.
β1 measures the ceteris paribus effect
of x1 on x2.
 Question 78
0 out of 1 points
Consider the following equation for household consumption expenditure:

Consmptn= β0+ β1Inc + β2Consmptn-1+ u

where ‘Consmptn’ measures the monthly consumption expenditure of a household,

‘Inc’ measures household income and ‘Consmptn-1’ is the consumption expenditure
in the previous month. Consmptn-1 is a _____ variable.
Selected
Answer:
exogenous

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Answers: exogenous
binary variable

lagged
dependent
proxy variable
 Question 79
0 out of 1 points
2
Consider the following regression model: log(y) = β0 + β1x1 + β2x1 + β3x3 + u. This
model will suffer from functional form misspecification if _____.
Selected
Answer:
x3 is a binary variable
Answers: x3 is a binary variable
u is heteroskedastic
β0 is omitted from the model

x12 is omitted from the model

 Question 80
0 out of 1 points
If j, an unbiased estimator of j, is also a consistent estimator of , then when
j
the sample size tends to infinity:
Selected
Answer:
the distribution of j diverges away from j.
Answers:
the distribution of j diverges away from j.

the distribution of j collapses to the single point j .

the distribution of j collapses to a single value of zero.

the distribution of j diverges away from a single value of zero.

 Question 81
0 out of 1 points
Consider the equation, Y = β1 + β2X2 + u. A null hypothesis,
H0: β2 = 0 states that:
Selected
Answer:
X2 has no effect on the expected value of
β2 .
Answers: X2 has no effect on the expected value of

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β2 .

X2 has no effect on the expected value of Y.

β2 has no effect on the expected value of Y.
Y has no effect on the expected
value of X2.
 Question 82
1 out of 1 points
Which of the following statements is true when the
dependent variable, y > 0?
Selected
Answer:
Models using log(y) as the dependent variable will
satisfy CLM assumptions more closely than
models using the level of y.
Answers:
Models using log(y) as the dependent variable will
satisfy CLM assumptions more closely than
models using the level of y.
Taking logarithmic form of variables make the
slope coefficients more responsive to rescaling.
Taking log of variables make OLS estimates more
sensitive to extreme values.
Taking log of a variable often expands its range.
 Question 83
0 out of 1 points
In the following equation, gdp refers to gross domestic
product, and FDI refers to foreign direct investment.

log(gdp) = 2.65 + 0.527log(bankcredit) + 0.222FDI

(0.13) (0.022) (0.017)
Which of the following statements is then true?
Selected
Answer:
If gdp increases by 1%, bank credit increases by
0.527%, the level of FDI remaining constant.
Answers: If gdp increases by 1%, bank credit increases by
0.527%, the level of FDI remaining constant.
If bank credit increases by 1%, gdp increases by
log(0.527)%, the level of FDI remaining constant.

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If bank credit increases by 1%, gdp increases by

0.527%, the level of FDI remaining constant.
If gdp increases by 1%, bank credit increases by
log(0.527)%, the level of FDI remaining constant.
 Question 84
0 out of 1 points
Find the degrees of freedom in a regression model that has
10 observations and 7 independent variables.
Selected
Answer:
1
7
Answers: 1
7
4
3

2
 Question 85
0 out of 1 points
Which of the following statements is true?
Selected
Answer:
Degrees of freedom of a restricted model is
always less than the degrees of freedom of an
unrestricted model.
Answers: Degrees of freedom of a restricted model is
always less than the degrees of freedom of an
unrestricted model.

The F statistic is always nonnegative as SSER is

never smaller than SSEU.
The F statistic is more flexible than the t statistic
to test a hypothesis with a single restriction.
If the calculated value of F statistic is higher than
the critical value, we reject the alternative
hypothesis in favor of the null hypothesis.
 Question 86
0 out of 1 points

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Which of the following is assumed for establishing the unbiasedness of Ordinary

Least Square (OLS) estimates?

Selected
Answer: The sample outcomes on the explanatory variable are all the
same value
Answers: The sample outcomes on the explanatory variable are all the
same value

The error term has the same variance given any value of the
explanatory variable
The regression equation is linear in the explained and
explanatory variables
The error term has an expected value of 1 given any value of the
explanatory variable
 Question 87
1 out of 1 points
In a regression model, if variance of the dependent variable, y, conditional
on an explanatory variable, x, or Var(y|x), is not constant, _____.
Selected
Answer:
the t statistics and confidence intervals are both
invalid no matter how large the sample size is
Answers:
the t statistics and confidence intervals are both
invalid no matter how large the sample size is
the t statistics are invalid and confidence
intervals are valid for small sample sizes
the t statistics confidence intervals are valid no
matter how large the sample size is
the t statistics are valid and confidence intervals
are invalid for small sample sizes
 Question 88
1 out of 1 points
The term u in an econometric model is usually referred to as the _____
Selected
Answer:
error term
Answers:
error term
parameter
hypothesis
dependent variable

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 Question 1
0 out of 1 points
What is the null hypothesis for a two-sided t-test?
Selected
Answer:
βk ≠
0
Answers:
βκ =
0
βk ≠
0
βk >
0
βk <
0
 Question 2
1 out of 1 points
How do you reduce the probability of committing a Type I
error?
Selected
Answer:
reduce α
Answers: increase α

reduce α
use a two-tailed test
increase the rejection
region
 Question 3
1 out of 1 points
A large p-value implies ____________.
Selected
Answer:
that the observed value for a parameter is
consistent with the null hypothesis.
Answers:
that the observed value for a parameter is
consistent with the null hypothesis.
a large value for a parameter

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a large t-statistic.
rejection of the null hypothesis.
 Question 4
1 out of 1 points
A researcher was determining the demand for train trip fits
the model below for 32 Local Government Areas in the
Greater Sydney Region where i represents the area.

Where:
total train trips taken from area i
train fare charged in area i
bus fare charged in area i
median income in ($) for area i
number of households without a car in area i

The STATA output is shown below.

. summarize y x1 x2 x3 x4

Variable | Obs Mean Std. Dev. Min Max

-------------+---------------------------------------------------------
y | 32 30024.03 23056.28 108 90357
x1 | 32 4.323125 .8523835 3.38 6.46
x2 | 32 3.75 .4399413 3.5 4.5
x3 | 32 54474 9174.191 40890 74076
x4 | 32 5233.531 6236.541 371 34688

. regress y x1 x2 x3 x4

Source | SS df MS Number of obs = 32

-------------+---------------------------------- F(4, 27) = 5.68
Model | 7.5334e+09 4 1.8833e+09 Prob >
F = 0.0019
Residual | 8.9460e+09 27 331333392 R-
squared = 0.4571
-------------+---------------------------------- Adj R-squared = 0.3767
Total | 1.6479e+10 31 531592136 Root
MSE = 18203

------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
x1 | -7507.723 4358.45 -1.72 0.096 -
16450.52 1435.077

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x2 | 17.75642 7732.101 0.00 0.998 -

15847.2 15882.72
x3 | -1.227367 .4097248 -3.00 0.006 -
2.068053 -.3866813
x4
| 1.454988 .5623023 2.59 0.015 .3012394 2.608738
_cons
| 121659.1 48607.23 2.50 0.019 21925.35 221392.9
------------------------------------------------------------------------------

Which of the following will be the correct conclusion about

bus fares?
Selected
Answer:
The bus fare is not a significant variable in
determining the demand for train trips and bus
transport is a complement for the train.
Answers: The bus fare is a significant variable in
determining the demand for train trips and both
modes of transports are complementary.
No conclusion can be made about bus fares
The bus fare is a significant variable in
determining the demand for train trips and bus
transport is a substitute for the train.

The bus fare is not a significant variable in

determining the demand for train trips and bus
transport is a complement for the train.
Response Your are
Feedback:
correct!
 Question 5
1 out of 1 points
Below is the model estimated showing the relationship
between the number of bus trips (bus) and the median
income (income) of 32 local government areas in the
Greater Sydney Region.

Which of the following is the correct interpretation for the

coefficient of income?

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Selected
Answer:
If income rises by $1.00, the change in the bus
trips will drop by 0.00267%
Answers: The income elasticity of bus trips is -0.0000267.
Taking bus trips is an inferior good.
The coefficient can not be interpreted.
If income rises by $1.00, the change in the bus
trips will be 2.67 × 10-5

If income rises by $1.00, the change in the bus

trips will drop by 0.00267%
Response You are
Feedback:
correct!
 Question 6
1 out of 1 points
Below is the estimated model showing the relationship of
the total dollar sales made from clothing stores (revenue),
average weekly wage (wage) and the total count of
unemployed individuals (unemployed) for all states in
Australia from 1994 to 2018.

What would be the null hypothesis be for the standard

regression F-Test of this model?
Selected
Answer:
and
Answers:

and

and an
d
The F-test can't be applied to this model.

or
Response You are
Feedback:
correct!

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 Question 7
1 out of 1 points
Which of the following is the least likely to be quantitative?
Selected
Answer:
gender
Answers: educati
on

gender
height
weight
 Question 8
1 out of 1 points
To provide quantitative answers to policy questions
_______________.
Selected
Answer:
you should examine empirical evidence.
Answers: It is typically impossible since policy questions
are not quantifiable.

you should examine empirical evidence.

you should interview the policy makers involved.
it is typically sufficient to use common sense.
 Question 9
1 out of 1 points
Imperfect multicollinearity _______ .
Selected
Answer:
implies that it will be difficult to estimate precisely
one or more of the partial effects using the data
at hand
Answers: suggests that a standard spreadsheet program
does not have enough power to estimate the
multiple regression model
violates one of the four Least Squares
assumptions in the multiple regression model
means that you cannot estimate the effect of at
least one of the Xs on Y

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implies that it will be difficult to estimate precisely

one or more of the partial effects using the data
at hand
 Question 10
1 out of 1 points
All of the following are true, with the exception of one
condition:
Selected
Answer:
a high or always means that an added variable is
statistically significant
Answers:

a high or always means that an added variable is

statistically significant

a high or does not mean that there is no omitted

variable bias

a high or does not mean that the regressors are a

true cause of the dependent variable

a high or does not necessarily mean that you

have the most appropriate set of regressors
 Question 11
1 out of 1 points
When an error term is added to an economic model and
assumptions about the distribution of the error term are
made, the resulting model is ______________.
Selected
Answer:
an econometric model that can be estimated
and used for inference.
Answers: fallacious, you should not make assumptions
about error terms.

an econometric model that can be estimated

and used for inference.
misspecified due to missing information.
heteroskedastic since error terms are no longer
random.
 Question 12
1 out of 1 points

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To derive the least squares estimator YP where P represents

the population, you find the estimator m which minimizes
Selected
Answer:

Answers:

Response Your answer was correct.

Feedback:
 Question 13
1 out of 1 points
A researcher estimated the model below showing the
relationship between total tonnes of crop produced in
thousands (crop), the total hectares of arable land for crops
(arable), the total head count employed in agriculture in
thousands (labour) and total subsidies in millions of US
dollars (support).

The researcher tested for multicollinearity as shown in the STATA

output below.
. estat vif

Variable | VIF 1/VIF

-------------+----------------------
Log_support | 11.20 0.089325
Log_Labour | 9.98 0.100199
Log_arable | 1.44 0.694259
-------------+----------------------
Mean VIF | 7.54

Which of the following statements is true about the

collinearity of the independent variables used?

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Selected
Answer:
Arable land has moderate multicollinearity while the
other variables either have serious or severe
multicollinearity.
Answers: There is no problem with multicollinearity for all
independent variables
The number employed and the amount of subsidies have
serious and severe multicollinearity, however it is still
possible to differentiate their effects from one another.

Arable land has moderate multicollinearity while the

other variables either have serious or severe
multicollinearity.
Response You are
Feedback:
correct!
 Question 14
1 out of 1 points
Below is a model showing the relationship between
subsidies and the workforce in the agricultural industry of
seven countries from 1991 to 2014. Amount of financial
support (support) is in millions and the number employed in
agriculture (labour) is in thousands.

Selected
Answer:
If 1,000 more were employed, the financial
support given to the sector will rise by $14.03
million
Answers: If 1,000 more were employed, the financial
support will rise by 14.03%
If 1,000 more were employed, the financial
support will rise by 140.32%

If 1,000 more were employed, the financial

support given to the sector will rise by $14.03
million
Response You are
Feedback:
correct!
 Question 15

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1 out of 1 points
While working with the sales manager of your firm you have
estimated the following model of sales volume as a function
of monthly household income:

Where Q is monthly sales volume, I is monthly household

income in thousands, and standard errors are listed below
the parameter estimates. How would you interpret the
coefficient for income?
Selected
Answer:
The relative elasticity of sales volume with
respect to household income is 1.212
Answers:
The relative elasticity of sales volume with
respect to household income is 1.212
Sales volume increases by 121.2 percentage
points when household income rises by $1,000
If sales volume was measured in dollars, a 1% rise in
household income will lead to a drop of $1.212
If sales volume was measured in dollars, a 1%
rise in household income will lead to a rise of
$1.212
Response You are
Feedback:
correct!
 Question 16
1 out of 1 points
For a normal distribution, the skewness and kurtosis
measures are as follows:
Selected
Answer:
0 and 3
Answers: 1.96 and
4
1 and 2
0 and 0

0 and 3
Response Your answer was correct!
Feedback:
 Question 17

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1 out of 1 points
A researcher was determining the demand for train trip fits
the model below for 32 Local Government Areas in the
Greater Sydney Region where i represents the area.

Where:
total train trips taken from area i
train fare charged in area i
bus fare charged in area i
median income in ($) for area i
number of households without a car in area i

The STATA output is shown below.

. summarize y x1 x2 x3 x4

Variable | Obs Mean Std. Dev. Min Max

-------------+---------------------------------------------------------
y | 32 30024.03 23056.28 108 90357
x1 | 32 4.323125 .8523835 3.38 6.46
x2 | 32 3.75 .4399413 3.5 4.5
x3 | 32 54474 9174.191 40890 74076
x4 | 32 5233.531 6236.541 371 34688

. regress y x1 x2 x3 x4

Source | SS df MS Number of obs = 32

-------------+---------------------------------- F(4, 27) = 5.68
Model | 7.5334e+09 4 1.8833e+09 Prob >
F = 0.0019
Residual | 8.9460e+09 27 331333392 R-
squared = 0.4571
-------------+---------------------------------- Adj R-squared = 0.3767
Total | 1.6479e+10 31 531592136 Root
MSE = 18203

------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
x1 | -7507.723 4358.45 -1.72 0.096 -
16450.52 1435.077
x2 | 17.75642 7732.101 0.00 0.998 -
15847.2 15882.72
x3 | -1.227367 .4097248 -3.00 0.006 -
2.068053 -.3866813
x4

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| 1.454988 .5623023 2.59 0.015 .3012394 2.608738

_cons
| 121659.1 48607.23 2.50 0.019 21925.35 221392.9
------------------------------------------------------------------------------

Which of the following is the correct conclusion about the intercept?

Selected
Answer:
We can reject the null hypothesis and, therefore,
conclude that there is sufficient evidence to show
that the intercept is different from zero.
Answers: We can reject the null hypothesis and, therefore,
conclude that there is sufficient evidence to show
that the intercept is not different from zero.
We cannot reject the null hypothesis and,
therefore, conclude that there is sufficient
evidence to show that the intercept is not different
from zero.

We can reject the null hypothesis and, therefore,

conclude that there is sufficient evidence to show
that the intercept is different from zero.
Response You are
Feedback:
correct!
 Question 18
1 out of 1 points

The estimators and determined by OLS will be the Best Linear

Unbiased Estimators (BLUE) if which of the following assumptions
hold?
(I) The errors have zero mean
(II) The variance of the errors is constant and finite over all values of the
independent variable(s)
(III) The errors are linearly independent of one another
(IV)There is no relationship between the error and corresponding
independent variables
Selected
Answer:
I, II, III and IV
Answers: I, II and III only

I, II, III and IV

I and II only

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II, III and IV only

 Question 19
1 out of 1 points
The following are all least squares assumptions with the
exception of:
Selected
Answer:
The explanatory variable in regression model is
normally distributed.
Answers: The conditional distribution of ui given Xi has a mean of
zero.

The explanatory variable in regression model is

normally distributed.
(Xi, Yi), i = 1,..., n are independently and identically
distributed.
none of the above
Response You are
Feedback:
correct!
 Question 20
1 out of 1 points
Below is the Cobb-Douglas function showing the
relationship between output (Q), capital (K) and labour (L).

What can you do to estimate this model using least

squares?
Selected
Answer:
Transform the function into its log form.
Answers:
Transform the function into its log form.
Get a large sample so you do not have to assume
the error terms are normally distributed
Estimate a linear function using subsets of the
data at a time
It cannot be done. Another estimation technique
needs to be employed.
Response You are
Feedback:
correct!
 Question 21

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1 out of 1 points
The model below uses data from all Australian states from
1994 to 2018. It shows the relationship between the total
sales made by cafes, restaurants and takeaways in millions
(foodservice) against the total head count of those
unemployed in thousands (unemployed).

If the number of unemployed drops by 2,000, what is the

expected change in the turnover for cafes, restaurants and
takeaways?
Selected
Answer:
Total sales increases by
2.64932%
Answers: Total sales decreases by
$26,493.20
Total sales increases by
1.32466%

Total sales increases by

2.64932%
Total sales drops by 2.64932%
Response You are
Feedback:
correct!
 Question 22
1 out of 1 points
You estimate a simple linear regression model using a
sample of 62 observations and obtain the following results
(estimated standard errors in parentheses below coefficient
estimates):
y = 97.25 + 33.74* x
(3.86) (9.42)

Which of the following will be the correct conclusion about

x?
Selected
Answer:
x is significant at 5%. It does have a relationship
with y.
Answers: No conclusion can be made about x

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x is significant at 5%. It does have a relationship

with y.
x is significant at 5% but it will have no effect on
y.
x is not a significant at 5%. Any change in x will
not bring a change in y.
Response You are
Feedback:
correct!
 Question 23
1 out of 1 points
You estimate 4 different specifications of an econometric
model by adding a variable each time and get the following
results.
R2 adj R2 AIC
Model A 0.3256 0.2285 22.56
Model B 0.5689 0.5394 22.37
Model C 0.3256 0.2916 21.21
Model D 0.3299 0.2911 21.79

Which model appears to be correctly specified?

Selected
Answer:
C
Answers: A
B

C
D
Response You are
Feedback: correct!

 Question 24
1 out of 1 points
A data set containing the number of adults with university
degrees in each regional area and greater capital city
regions throughout Australia in 2009 is ____________ .
Selected
Answer:
cross-section
data

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Answers: panel data

time series
data
flow data

cross-section
data
 Question 25
1 out of 1 points

A researcher estimated the model below showing the

relationship between total tonnes of crop produced in
thousands (crop), the total hectares of arable land for crops
(arable), the total head count employed in agriculture in
thousands (labour) and total subsidies in millions of US
dollars (support).

The researcher was not sure about the functional form. A

Ramsay RESET test was run as shown in the STATA output
below.
. estat ovtest

Ramsey RESET test using powers of the fitted values of

Log_crop
Ho: model has no omitted variables
F(3, 137) = 71.67
Prob > F = 0.0000

Which of the following statements is correct?

Selected d.
Answer:
Statement b & c both apply to the researcher
Answers: a.
The test shows that there is no problem with the model
b.
The test shows that the researcher needs to reconsider
how to account the relationship between crop output
and the other independent variable
c.

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The test shows that the researcher should include other

independent variables
d.
Statement b & c both apply to the researcher
Response You are
Feedback:
correct!
 Question 26
1 out of 1 points
A researcher was determining the demand for train trip fits
the model below for 32 Local Government Areas in the
Greater Sydney Region where i represents the area.

Where:
total train trips taken from area i
train fare charged in area i
bus fare charged in area i
median income in ($) for area i
number of households without a car in area i

The STATA output is shown below.

. summarize y x1 x2 x3 x4

Variable | Obs Mean Std. Dev. Min Max

-------------+---------------------------------------------------------
y | 32 30024.03 23056.28 108 90357
x1 | 32 4.323125 .8523835 3.38 6.46
x2 | 32 3.75 .4399413 3.5 4.5
x3 | 32 54474 9174.191 40890 74076
x4 | 32 5233.531 6236.541 371 34688

. regress y x1 x2 x3 x4

Source | SS df MS Number of obs = 32

-------------+---------------------------------- F(4, 27) = 5.68
Model | 7.5334e+09 4 1.8833e+09 Prob >
F = 0.0019
Residual | 8.9460e+09 27 331333392 R-
squared = 0.4571
-------------+---------------------------------- Adj R-squared = 0.3767
Total | 1.6479e+10 31 531592136 Root
MSE = 18203

------------------------------------------------------------------------------

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y | Coef. Std. Err. t P>|t| [95% Conf.

Interval]
-------------+----------------------------------------------------------------
x1 | -7507.723 4358.45 -1.72 0.096 -
16450.52 1435.077
x2 | 17.75642 7732.101 0.00 0.998 -
15847.2 15882.72
x3 | -1.227367 .4097248 -3.00 0.006 -
2.068053 -.3866813
x4
| 1.454988 .5623023 2.59 0.015 .3012394 2.608738
_cons
| 121659.1 48607.23 2.50 0.019 21925.35 221392.9
------------------------------------------------------------------------------

Which of the following will be the correct conclusion about

Income?
Selected
Answer:
Income is a significant variable in determining the
demand for train trips and taking the train is an
inferior good.
Answers:
Income is a significant variable in determining the
demand for train trips and taking the train is an
inferior good.
No conclusion can be made about Income
Income is not a significant variable in determining
the demand for train trips and taking the train is
considered a normal good.
Income is a significant variable in determining the
demand for train trips and taking the train is
considered a normal good.
Response You are
Feedback:
correct!
 Question 27
1 out of 1 points
The STATA output below uses data from all Australian states
from 1994 to 2018. It shows the relationship between the
total sales made by cafes, restaurants and takeaways in
millions (foodservice) against the total head count of those
unemployed in thousands (unemployed). The econometric
model and STATA output is shown below.

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 lOMoARcPSD|15697318

Econometric Model

STATA output
. regress Log_foodservice unemployed

Source | SS df MS Number of obs = 392

-------------+---------------------------------- F(1, 390) = 32.73
Model | 52.6771219 1 52.6771219 Prob >
F = 0.0000
Residual | 627.773093 390 1.6096746 R-
squared = 0.0774
-------------+---------------------------------- Adj R-squared = 0.0750
Total | 680.450215 391 1.74028188 Root
MSE = 1.2687

------------------------------------------------------------------------------
Log_foodse~e | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
unemployed | -.0132466 .0023156 -
5.72 0.000 -.0177992 -.008694
_cons
| 6.400111 .2704305 23.67 0.000 5.868427 6.93179
5
------------------------------------------------------------------------------

Which of the following statements are true?

Selected
Answer:
All of the above
Answers: The number of unemployed is significant at 10%.
It does affect the turnover of cafes, restaurants
and takeaways.
The number of unemployed is significant at 5%. It
does affect the turnover of cafes, restaurants and
takeaways.
The number of unemployed is significant at 1%. It
does affect the turnover of cafes, restaurants and
takeaways.

All of the above

Response You are
Feedback:
correct!
 Question 28

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1 out of 1 points
Suppose you are a potential college student that is
interested in determining whether it is worthwhile to
declare a certain major. In an effort to find the answer, you
collect data on 1,247 recent graduates on their grade point
average (GPA), their hours of study (StudyHours) and their
major. You create indicator variables to show if they
majored in the Sciences (Science), Engineering
(Engineering) or English (English).

Which of the following will be the correct conclusion about

hours of study and Salary?
Selected
Answer:
If the graduate studied 1% more hours, their
salary will be 0.02% higher under ceteris paribus
effects.
Answers:
If the graduate studied 1% more hours, their
salary will be 0.02% higher under ceteris paribus
effects.
If the graduate studied 1% more hours, their
salary will be 0.0002% higher under ceteris
paribus effects.
If the graduate studied 1% more hours, their
salary will be 2% higher under ceteris paribus
effects.
Response You are
Feedback:
correct!
 Question 29
1 out of 1 points
A researcher was determining the demand for train trip fits
the model below for 32 Local Government Areas in the
Greater Sydney Region where i represents the area.

Where:

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 lOMoARcPSD|15697318

total train trips taken from area i

train fare charged in area i
bus fare charged in area i
median income in ($) for area i
number of households without a car in area i

The STATA output is shown below.

. summarize y x1 x2 x3 x4

Variable | Obs Mean Std. Dev. Min Max

-------------+---------------------------------------------------------
y | 32 30024.03 23056.28 108 90357
x1 | 32 4.323125 .8523835 3.38 6.46
x2 | 32 3.75 .4399413 3.5 4.5
x3 | 32 54474 9174.191 40890 74076
x4 | 32 5233.531 6236.541 371 34688

. regress y x1 x2 x3 x4

Source | SS df MS Number of obs = 32

-------------+---------------------------------- F(4, 27) = 5.68
Model | 7.5334e+09 4 1.8833e+09 Prob >
F = 0.0019
Residual | 8.9460e+09 27 331333392 R-
squared = 0.4571
-------------+---------------------------------- Adj R-squared = 0.3767
Total | 1.6479e+10 31 531592136 Root
MSE = 18203

------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
x1 | -7507.723 4358.45 -1.72 0.096 -
16450.52 1435.077
x2 | 17.75642 7732.101 0.00 0.998 -
15847.2 15882.72
x3 | -1.227367 .4097248 -3.00 0.006 -
2.068053 -.3866813
x4
| 1.454988 .5623023 2.59 0.015 .3012394 2.608738
_cons
| 121659.1 48607.23 2.50 0.019 21925.35 221392.9
------------------------------------------------------------------------------

Which of the following will be the correct conclusion about

the model?
Selected
Answer:
The model explained 45.71% of the variation in

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Answers:
The model explained 45.71% of the variation in
train trips. Considering the number of
independent variables, it only explains 37.67%.
The total variation explained by the model is
14.63% (0.4571 × 32).
No conclusions can be made about the model.
The independent variables jointly do not explain
the number to total train trips.
Response You are
Feedback:
correct!
 Question 30
1 out of 1 points
Suppose you are a potential college student that is
interested in determining whether it is worthwhile to
declare a certain major. In an effort to find the answer, you
collect data on 1,247 recent graduates on their grade point
average (GPA), their hours of study (StudyHours) and their
major. You create indicator variables to show if they
majored in the Sciences (Science), Engineering
(Engineering) or English (English).

What is the meaning of the coefficient for the grade point

average?
Selected
Answer: If the graduate’s GPA was higher by 1 point, their salary will be
3% higher under ceteris paribus effects.
Answers:
If the graduate’s GPA was higher by 1 point, their salary will be
3% higher under ceteris paribus effects.
A graduate’s salary will be 0.03% if their GPA was 1 point higher.
Each additional point in their GPA will lead to a higher salary by
0.0003%.
none of the above
Response You are
Feedback:
correct!

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2. Module 05 - Model Specification and Multicollinearity

 Question 1
1 out of 1 points
What is an interaction term?
Selected
Answer:
an additional variable that is the product of 2 other independent
variables
Answers:
an additional variable that is the product of 2 other independent
variables
the expected value formed by multiplying a variable by its
estimated coefficient
a variable indicating an observation may be in the dataset
multiple times
a variable indicating 2 observations are related

Response Correct!
Feedback:

 Question 2
1 out of 1 points
How do you interpret the estimated value of β1 in the following model?

Selected
Answer:
the elasticity of y with respect to x1

Answers: the slope of the line representing the relationship

between y and x1

the elasticity of y with respect to x1

cannot be determined without more information

the mean value of ln(y) when ln(x1) = 0.
Response Correct!
Feedback:

 Question 3
1 out of 1 points
You have estimated the simple regression model below.

What is the elasticity when x1 = 8.49?

Selected

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Answer: 2.10
Answers:
2.10
263.19
-24.70
311.39

Response Correct!
Feedback:

 Question 4
1 out of 1 points
If your regression results show a high R2, adj R2, and a significant F-test,
but low t values for the coefficients, what is the most likely cause?
Selected
Answer:
collinearity
Answers: irrelevant variables included

omitted relevant variables

heteroskedasiticity

collinearity
Response Correct!
Feedback:

 Question 5
1 out of 1 points
You estimate 4 different specifications of an econometric model by adding a variable each
time and get the following results

Model A 0.3458 0.3285 22.56

Model B 0.3689 0.3394 22.37
Model C 0.4256 0.3916 21.21
Model D 0.4299 0.3911 21.79

Which model appears to be correct specified?

Selected
Answer:
C
Answers: A

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C
D
Response Correct!
Feedback:

 Question 6
1 out of 1 points
Omitted variable bias is a problem because _______ .
Selected
Answer:
it prevents correctly estimating marginal effects.
Answers: it prevents the model from being able to be estimated by ordinary least
squares.

it prevents correctly estimating marginal effects.

it causes the model to no longer be linear in the parameters.

it causes perfect multicollinearity.

Response Correct!
Feedback:

 Question 7
1 out of 1 points
If you reject the null hypothesis when performing a RESET test, what
should you conclude?
Selected
Answer:
the original model is incorrectly specified and can be improved
upon
Answers: an incorrect functional form was used

relevant variable are omitted and the coefficient estimates of

included variables are biased
at least one of the original coefficients is not equal to zero

the original model is incorrectly specified and can be improved

upon
Response Correct!
Feedback:

 Question 8
1 out of 1 points

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You have estimated a model of two variables shown below.

If x1 decreases by 2 units, what is the expected change in y?

Selected
Answer:
y increases by 8 percent.

Answers:
y increases by 8 percent.

y decreases by 8
percent.

y decreases by .08 units.

y increases by 4 units.

Response Correct!
Feedback:

 Question 9
1 out of 1 points
How can you estimate non-linear function forms using least squares?
Selected
Answer:
transform, such as squaring or cubing, some explanatory
variables

Answers: estimate the linear approximation over small ranges at a time

It cannot be done. You need to use another estimation

technique.

transform, such as squaring or cubing, some explanatory

variables

use a very large sample so you do not have to assume the error
terms are normally distributed

Response Correct!
Feedback:

 Question 10
1 out of 1 points
Which of the following is true?
Selected
Answer:

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A functional form misspecification can occur if the level of a variable is

used when the logarithm is more appropriate.
Answers:
A functional form misspecification can occur if the level of a variable is
used when the logarithm is more appropriate.
A functional form misspecification does not lead to biasedness in the
ordinary least squares estimators.

A functional form misspecification occurs only if a key variable

is uncorrelated with the error term.

A functional form misspecification does not lead to inconsistency in the

ordinary least squares estimators.

Response Correct!
Feedback:

 Question 11
1 out of 1 points
How does including an irrelevant variable in a regression model affect the
estimated coefficient of other variables in the model?
Selected
Answer:
they are unbiased but have larger standard errors
Answers:
they are unbiased but have larger standard errors
they are biased downward and have smaller standard errors
they are biased and the bias can be negative or positive
they are biased upward and have larger standard errors
Response Correct!
Feedback:

 Question 1
1 out of 1 points
How do you reduce the probability of committing a Type I
error?
Selected
Answer:
reduce α
Answers: increase the rejection
region
increase α
use a two-tailed test

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 lOMoARcPSD|15697318

reduce α
 Question 2
1 out of 1 points
The STATA output below uses data from all Australian states
from 1994 to 2018. It shows the relationship between the
total sales made by cafes, restaurants and takeaways in
millions (foodservice) against the total head count of those
unemployed in thousands (unemployed). The econometric
model and STATA output is shown below.

Econometric Model

STATA output
. regress Log_foodservice unemployed

Source | SS df MS Number of obs = 392

-------------+---------------------------------- F(1, 390) = 32.73
Model | 52.6771219 1 52.6771219 Prob >
F = 0.0000
Residual | 627.773093 390 1.6096746 R-
squared = 0.0774
-------------+---------------------------------- Adj R-squared = 0.0750
Total | 680.450215 391 1.74028188 Root
MSE = 1.2687

------------------------------------------------------------------------------
Log_foodse~e | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
unemployed | -.0132466 .0023156 -
5.72 0.000 -.0177992 -.008694
_cons
| 6.400111 .2704305 23.67 0.000 5.868427 6.93179
5
------------------------------------------------------------------------------

Which of the following statements are true?

Selected
Answer:
All of the above
Answers: The number of unemployed is significant at 10%.
It does affect the turnover of cafes, restaurants
and takeaways.
The number of unemployed is significant at 5%. It

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 lOMoARcPSD|15697318

does affect the turnover of cafes, restaurants and

takeaways.
The number of unemployed is significant at 1%. It
does affect the turnover of cafes, restaurants and
takeaways.

All of the above

Response You are
Feedback:
correct!
 Question 3
1 out of 1 points
You estimate 4 different specifications of an econometric
model by adding a variable each time and get the following
results.
R2 adj R2 AIC
Model A 0.3256 0.2285 22.56
Model B 0.5689 0.5394 22.37
Model C 0.3256 0.2916 21.21
Model D 0.3299 0.2911 21.79

Which model appears to be correctly specified?

Selected
Answer:
C
Answers: A
B

C
D
Response You are
Feedback: correct!

 Question 4
1 out of 1 points
You estimate a simple linear regression model using a
sample of 62 observations and obtain the following results
(estimated standard errors in parentheses below coefficient
estimates):
y = 97.25 + 33.74* x
(3.86) (9.42)

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Which of the following will be the correct conclusion about

x?
Selected
Answer:
x is significant at 5%. It does have a relationship
with y.
Answers: No conclusion can be made about x
x is significant at 5% but it will have no effect on
y.

x is significant at 5%. It does have a relationship

with y.
x is not a significant at 5%. Any change in x will
not bring a change in y.
Response You are
Feedback:
correct!
 Question 5
1 out of 1 points
Below is the Cobb-Douglas function showing the
relationship between output (Q), capital (K) and labour (L).

What can you do to estimate this model using least

squares?
Selected
Answer:
Transform the function into its log form.
Answers: Get a large sample so you do not have to assume
the error terms are normally distributed
It cannot be done. Another estimation technique
needs to be employed.

Transform the function into its log form.

Estimate a linear function using subsets of the
data at a time
Response You are
Feedback:
correct!
 Question 6
1 out of 1 points

A researcher estimated the model below showing the

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relationship between total tonnes of crop produced in

thousands (crop), the total hectares of arable land for crops
(arable), the total head count employed in agriculture in
thousands (labour) and total subsidies in millions of US
dollars (support).

The researcher was not sure about the functional form. A

Ramsay RESET test was run as shown in the STATA output
below.
. estat ovtest

Ramsey RESET test using powers of the fitted values of

Log_crop
Ho: model has no omitted variables
F(3, 137) = 71.67
Prob > F = 0.0000

Which of the following statements is correct?

Selected d.
Answer:
Statement b & c both apply to the researcher
Answers: a.
The test shows that there is no problem with the model
b.
The test shows that the researcher needs to reconsider
how to account the relationship between crop output
and the other independent variable
c.
The test shows that the researcher should include other
independent variables
d.
Statement b & c both apply to the researcher
Response You are
Feedback:
correct!
 Question 7
1 out of 1 points
A researcher was determining the demand for train trip fits
the model below for 32 Local Government Areas in the
Greater Sydney Region where i represents the area.

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 lOMoARcPSD|15697318

Where:
total train trips taken from area i
train fare charged in area i
bus fare charged in area i
median income in ($) for area i
number of households without a car in area i

The STATA output is shown below.

. summarize y x1 x2 x3 x4

Variable | Obs Mean Std. Dev. Min Max

-------------+---------------------------------------------------------
y | 32 30024.03 23056.28 108 90357
x1 | 32 4.323125 .8523835 3.38 6.46
x2 | 32 3.75 .4399413 3.5 4.5
x3 | 32 54474 9174.191 40890 74076
x4 | 32 5233.531 6236.541 371 34688

. regress y x1 x2 x3 x4

Source | SS df MS Number of obs = 32

-------------+---------------------------------- F(4, 27) = 5.68
Model | 7.5334e+09 4 1.8833e+09 Prob >
F = 0.0019
Residual | 8.9460e+09 27 331333392 R-
squared = 0.4571
-------------+---------------------------------- Adj R-squared = 0.3767
Total | 1.6479e+10 31 531592136 Root
MSE = 18203

------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
x1 | -7507.723 4358.45 -1.72 0.096 -
16450.52 1435.077
x2 | 17.75642 7732.101 0.00 0.998 -
15847.2 15882.72
x3 | -1.227367 .4097248 -3.00 0.006 -
2.068053 -.3866813
x4
| 1.454988 .5623023 2.59 0.015 .3012394 2.608738
_cons
| 121659.1 48607.23 2.50 0.019 21925.35 221392.9
------------------------------------------------------------------------------

Which of the following will be the correct conclusion about

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the model?
Selected
Answer:
The model explained 45.71% of the variation in
train trips. Considering the number of
independent variables, it only explains 37.67%.
Answers:
The model explained 45.71% of the variation in
train trips. Considering the number of
independent variables, it only explains 37.67%.
The total variation explained by the model is
14.63% (0.4571 × 32).
The independent variables jointly do not explain
the number to total train trips.
No conclusions can be made about the model.
Response You are
Feedback:
correct!
 Question 8
1 out of 1 points
Suppose you are a potential college student that is
interested in determining whether it is worthwhile to
declare a certain major. In an effort to find the answer, you
collect data on 1,247 recent graduates on their grade point
average (GPA), their hours of study (StudyHours) and their
major. You create indicator variables to show if they
majored in the Sciences (Science), Engineering
(Engineering) or English (English).

What is the meaning of the coefficient for the grade point

average?
Selected
Answer: If the graduate’s GPA was higher by 1 point, their salary will be
3% higher under ceteris paribus effects.
Answers:
If the graduate’s GPA was higher by 1 point, their salary will be
3% higher under ceteris paribus effects.
A graduate’s salary will be 0.03% if their GPA was 1 point higher.
Each additional point in their GPA will lead to a higher salary by

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0.0003%.
none of the above
Response You are
Feedback:
correct!
 Question 9
1 out of 1 points
A researcher was determining the demand for train trip fits
the model below for 32 Local Government Areas in the
Greater Sydney Region where i represents the area.

Where:
total train trips taken from area i
train fare charged in area i
bus fare charged in area i
median income in ($) for area i
number of households without a car in area i

The STATA output is shown below.

. summarize y x1 x2 x3 x4

Variable | Obs Mean Std. Dev. Min Max

-------------+---------------------------------------------------------
y | 32 30024.03 23056.28 108 90357
x1 | 32 4.323125 .8523835 3.38 6.46
x2 | 32 3.75 .4399413 3.5 4.5
x3 | 32 54474 9174.191 40890 74076
x4 | 32 5233.531 6236.541 371 34688

. regress y x1 x2 x3 x4

Source | SS df MS Number of obs = 32

-------------+---------------------------------- F(4, 27) = 5.68
Model | 7.5334e+09 4 1.8833e+09 Prob >
F = 0.0019
Residual | 8.9460e+09 27 331333392 R-
squared = 0.4571
-------------+---------------------------------- Adj R-squared = 0.3767
Total | 1.6479e+10 31 531592136 Root
MSE = 18203

------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+----------------------------------------------------------------

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x1 | -7507.723 4358.45 -1.72 0.096 -

16450.52 1435.077
x2 | 17.75642 7732.101 0.00 0.998 -
15847.2 15882.72
x3 | -1.227367 .4097248 -3.00 0.006 -
2.068053 -.3866813
x4
| 1.454988 .5623023 2.59 0.015 .3012394 2.608738
_cons
| 121659.1 48607.23 2.50 0.019 21925.35 221392.9
------------------------------------------------------------------------------

Which of the following will be the correct conclusion about

bus fares?
Selected
Answer:
The bus fare is not a significant variable in
determining the demand for train trips and bus
transport is a substitute for the train.
Answers: The bus fare is a significant variable in
determining the demand for train trips and bus
transport is a substitute for the train.
The bus fare is a significant variable in
determining the demand for train trips and both
modes of transports are complementary.

The bus fare is not a significant variable in

determining the demand for train trips and bus
transport is a substitute for the train.
No conclusion can be made about bus fares
Response Your are
Feedback:
correct!
 Question 10
1 out of 1 points
You estimate a simple linear regression model using a
sample of 62 observations and obtain the following results
(estimated standard errors in parentheses below coefficient
estimates):
y = 97.25 + 33.74* x
(3.86) (9.42)

Which of the following will be the correct conclusion about

x?
Selected

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Answer: x is significant at 5%. It does have a relationship

with y.
Answers: x is significant at 5% but it will have no effect on
y.
x is not a significant at 5%. Any change in x will
not bring a change in y.

x is significant at 5%. It does have a relationship

with y.
No conclusion can be made about x
Response You are
Feedback:
correct!
 Question 11
1 out of 1 points

The estimators and determined by OLS will be the Best Linear

Unbiased Estimators (BLUE) if which of the following assumptions
hold?
(I) The errors have zero mean
(II) The variance of the errors is constant and finite over all values of the
independent variable(s)
(III) The errors are linearly independent of one another
(IV)There is no relationship between the error and corresponding
independent variables
Selected
Answer:
I, II, III and IV
Answers: I and II only
I, II and III only

I, II, III and IV

II, III and IV only
 Question 12
1 out of 1 points
A researcher was determining the demand for train trip fits
the model below for 32 Local Government Areas in the
Greater Sydney Region where i represents the area.

Where:

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 lOMoARcPSD|15697318

total train trips taken from area i

train fare charged in area i
bus fare charged in area i
median income in ($) for area i
number of households without a car in area i

The STATA output is shown below.

. summarize y x1 x2 x3 x4

Variable | Obs Mean Std. Dev. Min Max

-------------+---------------------------------------------------------
y | 32 30024.03 23056.28 108 90357
x1 | 32 4.323125 .8523835 3.38 6.46
x2 | 32 3.75 .4399413 3.5 4.5
x3 | 32 54474 9174.191 40890 74076
x4 | 32 5233.531 6236.541 371 34688

. regress y x1 x2 x3 x4

Source | SS df MS Number of obs = 32

-------------+---------------------------------- F(4, 27) = 5.68
Model | 7.5334e+09 4 1.8833e+09 Prob >
F = 0.0019
Residual | 8.9460e+09 27 331333392 R-
squared = 0.4571
-------------+---------------------------------- Adj R-squared = 0.3767
Total | 1.6479e+10 31 531592136 Root
MSE = 18203

------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
x1 | -7507.723 4358.45 -1.72 0.096 -
16450.52 1435.077
x2 | 17.75642 7732.101 0.00 0.998 -
15847.2 15882.72
x3 | -1.227367 .4097248 -3.00 0.006 -
2.068053 -.3866813
x4
| 1.454988 .5623023 2.59 0.015 .3012394 2.608738
_cons
| 121659.1 48607.23 2.50 0.019 21925.35 221392.9
------------------------------------------------------------------------------

Which of the following is the correct conclusion about the intercept?

Selected
Answer:

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We can reject the null hypothesis and, therefore,

conclude that there is sufficient evidence to show
that the intercept is different from zero.
Answers: We cannot reject the null hypothesis and,
therefore, conclude that there is sufficient
evidence to show that the intercept is not different
from zero.

We can reject the null hypothesis and, therefore,

conclude that there is sufficient evidence to show
that the intercept is different from zero.
We can reject the null hypothesis and, therefore,
conclude that there is sufficient evidence to show
that the intercept is not different from zero.
Response You are
Feedback:
correct!
 Question 13
1 out of 1 points
The model below uses data from all Australian states from
1994 to 2018. It shows the relationship between the total
sales made by cafes, restaurants and takeaways in millions
(foodservice) against the total head count of those
unemployed in thousands (unemployed).

If the number of unemployed drops by 2,000, what is the

expected change in the turnover for cafes, restaurants and
takeaways?
Selected
Answer:
Total sales increases by
2.64932%
Answers: Total sales decreases by
$26,493.20
Total sales increases by
1.32466%

Total sales increases by

2.64932%

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 lOMoARcPSD|15697318

Total sales drops by 2.64932%

Response You are
Feedback:
correct!
 Question 14
1 out of 1 points
A researcher was determining the demand for train trip fits
the model below for 32 Local Government Areas in the
Greater Sydney Region where i represents the area.

Where:
total train trips taken from area i
train fare charged in area i
bus fare charged in area i
median income in ($) for area i
number of households without a car in area i

The STATA output is shown below.

. summarize y x1 x2 x3 x4

Variable | Obs Mean Std. Dev. Min Max

-------------+---------------------------------------------------------
y | 32 30024.03 23056.28 108 90357
x1 | 32 4.323125 .8523835 3.38 6.46
x2 | 32 3.75 .4399413 3.5 4.5
x3 | 32 54474 9174.191 40890 74076
x4 | 32 5233.531 6236.541 371 34688

. regress y x1 x2 x3 x4

Source | SS df MS Number of obs = 32

-------------+---------------------------------- F(4, 27) = 5.68
Model | 7.5334e+09 4 1.8833e+09 Prob >
F = 0.0019
Residual | 8.9460e+09 27 331333392 R-
squared = 0.4571
-------------+---------------------------------- Adj R-squared = 0.3767
Total | 1.6479e+10 31 531592136 Root
MSE = 18203

------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
x1 | -7507.723 4358.45 -1.72 0.096 -

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 lOMoARcPSD|15697318

16450.52 1435.077
x2 | 17.75642 7732.101 0.00 0.998 -
15847.2 15882.72
x3 | -1.227367 .4097248 -3.00 0.006 -
2.068053 -.3866813
x4
| 1.454988 .5623023 2.59 0.015 .3012394 2.608738
_cons
| 121659.1 48607.23 2.50 0.019 21925.35 221392.9
------------------------------------------------------------------------------

Which of the following will be the correct conclusion about

Income?
Selected
Answer:
Income is a significant variable in determining the
demand for train trips and taking the train is an
inferior good.
Answers: Income is a significant variable in determining the
demand for train trips and taking the train is
considered a normal good.
No conclusion can be made about Income
Income is not a significant variable in determining
the demand for train trips and taking the train is
considered a normal good.

Income is a significant variable in determining the

demand for train trips and taking the train is an
inferior good.
Response You are
Feedback:
correct!
 Question 15
1 out of 1 points
How do you reduce the probability of committing a Type I
error?
Selected
Answer:
reduce α
Answers: increase the rejection
region
use a two-tailed test

reduce α

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 lOMoARcPSD|15697318

increase α
 Question 16
1 out of 1 points
For a normal distribution, the skewness and kurtosis
measures are as follows:
Selected
Answer:
0 and 3
Answers: 1 and 2

0 and 3
0 and 0
1.96 and
4
Response Your answer was correct!
Feedback:
 Question 17
1 out of 1 points
To derive the least squares estimator YP where P represents
the population, you find the estimator m which minimizes
Selected
Answer:

Answers:

Response Your answer was correct.

Feedback:

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9/21/23, 1:30 PM Review Test Submission: Online Quiz 1 – ECON3006 (...

Hoang Khanh Linh Nguyen 160

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ECON3006 (Vietnam Session 3 2023) Economic and Financial Modelling

Assessment 3 (10%) - Online Quizzes Review Test Submission: Online Quiz 1

Review Test Submission: Online Quiz 1

User Hoang Khanh Linh Nguyen

Subject ECON3006 (Vietnam Session 3 2023) Economic and Financial Modelling
Test Online Quiz 1
Started 21/09/23 4:24 PM
Submitted 21/09/23 4:30 PM
Due Date 25/09/23 2:59 AM
Status Completed
Attempt Score 10 out of 10 points
Time Elapsed 6 minutes out of 20 minutes
Results All Answers, Submitted Answers, Correct Answers, Incorrectly Answered
Displayed Questions

Question 1 1 out of 1 points

A data set that consists of a sample of individuals, households, firms, cities,

states, countries, or a variety of other units, taken at a given point in time, is
called a(n) _____.

Selected Answer: cross-sectional data set

Answers: cross-sectional data set

longitudinal data set

time series data set

experimental data set

Question 2 1 out of 1 points

_____ has a causal effect on _____.

Selected Answer: Income; consumption

Answers: Income; unemployment

Height; health

Income; consumption

Age; wage
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9/21/23, 1:30 PM Review Test Submission: Online Quiz 1 – ECON3006 (...
Age; wage

Question 3 1 out of 1 points

Which of the following is a difference between panel and pooled cross-

sectional data?

Selected
Answer: A panel data set consists of data on the same cross-sectional
units over a given period of time while a pooled data set
consists of data on different cross-sectional units over a given
period of time

Answers: A panel data set consists of data on different cross-sectional

units over a given period of time while a pooled data set
consists of data on the same cross-sectional units over a given
period of time.

A panel data set consists of data on the same cross-sectional

units over a given period of time while a pooled data set
consists of data on different cross-sectional units over a given
period of time

A panel data consists of data on a single variable measured at a

given point in time while a pooled data set consists of data on
the same cross-sectional units over a given period of time.

A panel data set consists of data on a single variable measured

at a given point in time while a pooled data set consists of data
on more than one variable at a given point in time.

Question 4 1 out of 1 points

An empirical analysis relies on _____to test a theory.

Selected Answer: data

Answers: common sense

ethical considerations

data

customs and conventions

Question 5 1 out of 1 points

Which of the following is the first step in empirical economic analysis?

Selected Answer: Specification of an econometric model

Answers: Collection of data

Statement of hypotheses
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9/21/23, 1:30 PM Review Test Submission: Online Quiz 1 – ECON3006 (...

Specification of an econometric model

Testing of hypotheses

Question 6 1 out of 1 points

A data set that consists of observations on a variable or several variables over

time is called a _____ data set.

Selected Answer: time series

Answers: binary

cross-sectional

time series

experimental

Question 7 1 out of 1 points

Which of the following refers to panel data?

Selected
Answer: Data on the birth rate, death rate and population growth rate
in developing countries over a 10-year period.

Answers: Data on the unemployment rate in a country over a 5-year

period

Data on the birth rate, death rate and population growth rate
in developing countries over a 10-year period.

Data on the income of 5 members of a family on a particular

year.

Data on the price of a company's share during a year.

Question 8 1 out of 1 points

Nonexperimental data is called _____.

Selected Answer: observational data

Answers: cross-sectional data

time series data

observational data

panel data

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Question 9 1 out of 1 points

The term 'u' in an econometric model is usually referred to as the _____.

Selected Answer: error term

Answers: error term

parameter

hypothesis

dependent variable

Question 10 1 out of 1 points

The parameters of an econometric model _____.

Selected
Answer: describe the strength of the relationship between the variable
under study and the factors affecting it

Answers: include all unobserved factors affecting the variable being

studied

describe the strength of the relationship between the variable

under study and the factors affecting it

refer to the explanatory variables included in the model

refer to the predictions that can be made using the model

Thursday, 21 September 2023 4:30:40 PM AEST

← OK

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9/29/23, 10:05 PM Review Test Submission: Online Quiz 2 – ECON3006 (...
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ECON3006 (Vietnam Session 3 2023) Economic and Financial Modelling Assessment 3 (10%) - Online Quizzes Review Test Submission: Online Quiz 2

Review Test Submission: Online Quiz 2

User Hoang Khanh Linh Nguyen

Subject ECON3006 (Vietnam Session 3 2023) Economic and Financial Modelling
Test Online Quiz 2
Started 30/09/23 12:44 AM
Submitted 30/09/23 1:04 AM
Due Date 2/10/23 3:59 AM
Status Completed
Attempt Score 9 out of 10 points
Time Elapsed 20 minutes out of 20 minutes
Results Displayed All Answers, Submitted Answers, Correct Answers, Incorrectly Answered Questions

Question 1 1 out of 1 points

Suppose you collected data on weekly total household expenditure from 36 families living in Melbourne and the average expenditure is
$450. Suppose the variance of weekly household expenditure in Melbourne is known as $1764. Based on this set of information, which of
the following are the approximately correct upper and lower bounds for 99% confidence interval estimates of the population mean
household expenditure in Melbourne?

Selected Answer: [$432, $468]

Answers: [$750, $765]

[$432, $468]

[$555, $655]

[$179, $205]

Question 2 1 out of 1 points

Suppose that the salary of new fnance graduates in Australia with analytical skills is normally distributed with unknown mean µ in A$ and
variance. Suppose a sample of 25 business graduates is drawn and the sample mean is observed as X̅ = A$60000 with sample standard
deviation s = 1800. What would you conclude based on the null hypothesis, Ho: µ = A$61 200 against the alternative that H1: µ ≠ A$61 200
at the 5% level (2.5% in each tail)?

Selected Answer: Reject the null hypothesis at 5% level.

Answers: Do not reject the null hypothesis.

Reject the null hypothesis at 5% level.

The null hypothesis is statistically signifcant.

Need more information to make a decision.

Question 3 1 out of 1 points

Consider the following probability distribution for a discrete random variable X. What is E(3X2 – 4X + 2)?

Selected Answer: 19.25

Answers: 19.25

8.55

9.55

17.45

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Question 4 1 out of 1 points

Suppose Y is a random variable that represents the face number that shows up in an experiment of rolling a fair octahedron dice. What is
var(Y)? (Note: For an octahedron dice, X takes a value from 1 to 8.)

Selected Answer: 5.25

Answers: 5.25

4.50

6.50

4.75

Question 5 1 out of 1 points

Which one of the following statements is correct for the central limit theorem?

Selected
Answer: If the distribution of a variable X̅ is not normal, the sample mean is approximately normally distributed such that

for a sufficiently large sample.

Answers: The sample mean is the best linear unbiased estimator of the population mean.

The distribution of the sample mean is given by only when the variable X is normally distributed such

that , regardless of the sample size.

If the distribution of a variable X̅ is not normal, the sample mean is approximately normally distributed such that

for a sufficiently large sample.

If a variable , then the sample mean regardless of the sample size and the distribution of

any other variable Y.

Question 6 1 out of 1 points

The share price of Commonwealth Bank Australia is known to have a normal distribution with a mean of 70 dollars/day and standard
deviation σ = 4 dollars/day. What is the probability that in a sample of 64 days, the mean daily share price will exceed 71 dollars?

Selected Answer: 0.0228

Answers: 0.0228

0.9938

0.8413

None of the above

Question 7 1 out of 1 points

Suppose X and Y are two dependent discrete random variables. Let Z = (3X – 2Y + 4) with the following set of information provided:

E(X2) = 25 E(X) = 3 E(Y2) = 28 E(Y) = 4 E(XY) = 20

What is var(Z)?

Selected Answer: 96

Answers: 28

172

22

96

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9/29/23, 10:05 PM Review Test Submission: Online Quiz 2 – ECON3006 (...
Question 8 1 out of 1 points

Let the variable Y be normally distributed with mean 12 and variance 16. That is, Y ~ N(12, 16). What is the prob. (7 ≤ Y ≤ 22.2)?

Selected Answer: 0.8890

Answers: 0.9946

0.8890

.4878

.8944

Question 9 1 out of 1 points

A physical education professor at a university in New Zealand told his class that they could earn an A grade for the long-jump if they could
jump further than 7 metres. Suppose that the distances jumped by the students have a normal distribution with a mean of 5.75 metres and
a standard deviation of 50 centimetres. What percentage of his students will earn an A grade?

Selected Answer: 0062

Answers: .9686

.2486

.0228

0062

Question 10 0 out of 1 points

Consider the following table for the joint pdf of two discrete random variables X and Y:

What is the covariance between X and Y?

Selected Answer: 2.54

Answers: 2.54

1.85

.04

.12

Saturday, 30 September 2023 1:04:28 AM AEST

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10/6/23, 5:58 PM Review Test Submission: Online Quiz 3 (Chapter 4) – ...
Hoang Khanh Linh Nguyen 27
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ECON3006 (Vietnam Session 3 2023) Economic and Financial Modelling Assessment 3 (10%) - Online Quizzes
Review Test Submission: Online Quiz 3 (Chapter 4)

Review Test Submission: Online Quiz 3 (Chapter 4)

User Hoang Khanh Linh Nguyen

Subject ECON3006 (Vietnam Session 3 2023) Economic and Financial Modelling
Test Online Quiz 3 (Chapter 4)
Started 6/10/23 9:50 PM
Submitted 6/10/23 9:57 PM
Due Date 9/10/23 3:59 AM
Status Completed
Attempt Score 10 out of 10 points
Time Elapsed 7 minutes out of 20 minutes
Results Displayed All Answers, Submitted Answers, Correct Answers, Incorrectly Answered Questions

Question 1 1 out of 1 points

What does the equation denote if the regression equation is ?

Selected Answer: The sample regression function

Answers: The explained sum of squares

The total sum of squares

The sample regression function

The population regression function

Question 2 1 out of 1 points

Consider the following regression model: y = 0 + 1x1 + u. Which of the following is a property of Ordinary Least Square (OLS) estimates
of this model and their associated statistics?

Selected Answer: The point ( ) always lies on the OLS regression line.

Answers: The sum, and therefore the sample average of the OLS residuals, is positive.

The sum of the OLS residuals is negative.

The sample covariance between the regressors and the OLS residuals is positive.

The point ( ) always lies on the OLS regression line.

Question 3 1 out of 1 points

The explained sum of squares for the regression function, , is defined as _____.

Selected Answer:

Answers:

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10/6/23, 5:58 PM Review Test Submission: Online Quiz 3 (Chapter 4) – ...

Question 4 1 out of 1 points

In the regression of y on x, the error term exhibits heteroskedasticity if _____.

Selected Answer: Var(y|x) is a function of x

Answers: it has a constant variance

Var(y|x) is a function of x

x is a function of y

y is a function of x

Question 5 1 out of 1 points

Which of the following is a nonlinear regression model?

Selected Answer: y = 1 / (0 + 1x) + u

Answers:
y= 0 + 1x1/2 + u
log y = 0 + 1log x +u

y = 1 / (0 + 1x) + u

y = 0 + 1x + u

Question 6 1 out of 1 points

The sample correlation between xi and yi is denoted by _____.

Selected Answer:

Answers:

Question 7 1 out of 1 points

In the equation , what is the estimated value of ?

Selected Answer:

Answers:

Question 8 1 out of 1 points

If the residual sum of squares (SSR) in a regression analysis is 66 and the total sum of squares (SST) is equal to 90, what is the value of the
coefficient of determination?

Selected Answer: 0.27

Answers: 0.73

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10/6/23, 5:58 PM Review Test Submission: Online Quiz 3 (Chapter 4) – ...

0.55

0.27

1.2

Question 9 1 out of 1 points

Which of the following is assumed for establishing the unbiasedness of Ordinary Least Square (OLS) estimates?

Selected Answer: The error term has the same variance given any value of the explanatory variable.

Answers: The error term has an expected value of 1 given any value of the explanatory variable.

The regression equation is linear in the explained and explanatory variables.

The sample outcomes on the explanatory variable are all the same value.

The error term has the same variance given any value of the explanatory variable.

Question 10 1 out of 1 points

In a regression equation, changing the units of measurement of only the independent variable does not affect the _____.

Selected Answer: intercept

Answers: dependent variable

slope

intercept

error term

Friday, 6 October 2023 9:57:54 PM AEDT

← OK

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10/14/23, 10:07 PM Review Test Submission: Online Quiz 4 (Chapter 5) – ...
Hoang Khanh Linh Nguyen 29
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ECON3006 (Vietnam Session 3 2023) Economic and Financial Modelling Assessment 3 (10%) - Online Quizzes
Review Test Submission: Online Quiz 4 (Chapter 5)

Review Test Submission: Online Quiz 4 (Chapter 5)

User Hoang Khanh Linh Nguyen

Subject ECON3006 (Vietnam Session 3 2023) Economic and Financial Modelling
Test Online Quiz 4 (Chapter 5)
Started 13/10/23 11:39 PM
Submitted 13/10/23 11:51 PM
Due Date 16/10/23 3:59 AM
Status Completed
Attempt Score 10 out of 10 points
Time Elapsed 12 minutes out of 20 minutes
Results Displayed All Answers, Submitted Answers, Correct Answers, Incorrectly Answered Questions

Question 1 1 out of 1 points

Which of the following is true of BLUE?

Selected
Answer: An estimator is linear if and only if it can be expressed as a linear function of the data on the dependent
variable.
Answers: It is a rule that can be applied to any one value of the data to produce an estimate.

An estimator is an unbiased estimator of if for any .

An estimator is linear if and only if it can be expressed as a linear function of the data on the dependent
variable.
It is the best linear uniform estimator.

Question 2 1 out of 1 points

Find the degrees of freedom in a regression model that has 10 observations and 7 independent variables.

Selected Answer: 2

Answers: 17

Question 3 1 out of 1 points

If the explained sum of squares is 35 and the total sum of squares is 49, what is the residual sum of squares?

Selected Answer: 14

Answers: 84

13

83

14

Question 4 1 out of 1 points

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10/14/23, 10:07 PM Review Test Submission: Online Quiz 4 (Chapter 5) – ...

If an independent variable in a multiple linear regression model is an exact linear combination of other independent variables, the model
suffers from the problem of _____.

Selected Answer: perfect collinearity

Answers: perfect collinearity

homoskedasticity

heteroskedasticty

omitted variable bias

Question 5 1 out of 1 points

High (but not perfect) correlation between two or more independent variables is called _____.

Selected Answer: multicollinearity

Answers: heteroskedasticty

homoskedasticty

multicollinearity

micronumerosity

Question 6 1 out of 1 points

The Gauss-Markov theorem will not hold if _____.

Selected Answer: the independent variables have exact linear relationships among them

Answers: the error term has the same variance given any values of the explanatory variables

the error term has an expected value of zero given any values of the independent variables

the independent variables have exact linear relationships among them

the regression model relies on the method of random sampling for collection of data

Question 7 1 out of 1 points

Consider the following regression equation: y = β0 + β1x1 + β2x2 + u. What does β1 imply?

Selected Answer: β1 measures the ceteris paribus effect of x1 on y.

Answers: β1 measures the ceteris paribus effect of x1 on x2.

β1 measures the ceteris paribus effect of y on x1.

β1 measures the ceteris paribus effect of x1 on y.

β1 measures the ceteris paribus effect of x1 on u.

Question 8 1 out of 1 points

The assumption that there are no exact linear relationships among the independent variables in a multiple linear regression model fails if
_____, where n is the sample size and k is the number of parameters.

Selected Answer: n<k+1

Answers: n>2

n=k+1

n>k

n<k+1

Question 9 1 out of 1 points

Which of the following is true of R2?

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10/14/23, 10:07 PM Review Test Submission: Online Quiz 4 (Chapter 5) – ...

Selected
Answer:
R2 shows what percentage of the total variation in the dependent variable, Y, is explained by the explanatory variables.

Answers:
R2 is also called the standard error of regression.

A low R2 indicates that the Ordinary Least Squares line fits the data well.

R2 usually decreases with an increase in the number of independent variables in a regression.

R2 shows what percentage of the total variation in the dependent variable, Y, is explained by the explanatory variables.

Question 10 1 out of 1 points

Which of the following is true of R2?

Selected
Answer:
R2 shows what percentage of the total variation in the dependent variable, Y, is explained by the explanatory variables.

Answers:
R2 is also called the standard error of regression.

A low R2 indicates that the Ordinary Least Squares line fits the data well.

R2 usually decreases with an increase in the number of independent variables in a regression.

R2 shows what percentage of the total variation in the dependent variable, Y, is explained by the explanatory variables.

Sunday, 15 October 2023 2:07:33 AM AEDT

← OK

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10/18/23, 3:05 PM Review Test Submission: Online Quiz 5 (Chapter 6) – ...
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ECON3006 (Vietnam Session 3 2023) Economic and Financial Modelling Assessment 3 (10%) - Online Quizzes
Review Test Submission: Online Quiz 5 (Chapter 6)

Review Test Submission: Online Quiz 5 (Chapter 6)

User Hoang Khanh Linh Nguyen

Subject ECON3006 (Vietnam Session 3 2023) Economic and Financial Modelling
Test Online Quiz 5 (Chapter 6)
Started 18/10/23 6:51 PM
Submitted 18/10/23 7:05 PM
Due Date 23/10/23 3:59 AM
Status Completed
Attempt Score 10 out of 10 points
Time Elapsed 14 minutes out of 20 minutes
Results Displayed All Answers, Submitted Answers, Correct Answers, Incorrectly Answered Questions

Question 1 1 out of 1 points

Which of the following statements is true?

Selected The F statistic is always nonnegative as SSRr is never smaller than SSRur.
Answer:

Answers: If the calculated value of F statistic is higher than the critical value, we reject the alternative hypothesis in favor of the null
hypothesis.

The F statistic is always nonnegative as SSRr is never smaller than SSRur.

Degrees of freedom of a restricted model is always less than the degrees of freedom of an unrestricted model.

The F statistic is more flexible than the t statistic to test a hypothesis with a single restriction.

Question 2 1 out of 1 points

Consider the equation, y = α + β1x1 + β2x2 + u. A null hypothesis, H0: β2 = 0 states that:

Selected Answer: x2 has no effect on the expected value of y.

Answers: x2 has no effect on the expected value of β2.

x2 has no effect on the expected value of y.

β2 has no effect on the expected value of y.

y has no effect on the expected value of x2.

Question 3 1 out of 1 points

Which of the following is a statistic that can be used to test hypotheses about a single population parameter?

Selected Answer: t statistic

Answers: F statistic

t statistic

𝜒2 statistic

Durbin Watson statistic

Question 4 1 out of 1 points

Which of the following statements is true of hypothesis testing?

Selected Answer: A restricted model will always have fewer parameters than its unrestricted model.

Answers: The t test can be used to test multiple linear restrictions.

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A test of single restriction is also referred to as a joint hypotheses test.

A restricted model will always have fewer parameters than its unrestricted model.

OLS estimates maximize the sum of squared residuals.

Question 5 1 out of 1 points

The significance level of a test is:

Selected Answer: the probability of rejecting the null hypothesis when it is true.

Answers: the probability of rejecting the null hypothesis when it is false.

one minus the probability of rejecting the null hypothesis when it is false.

the probability of rejecting the null hypothesis when it is true.

one minus the probability of rejecting the null hypothesis when it is true.

Question 6 1 out of 1 points

The normality assumption implies that:

Selected
Answer: the population error u is independent of the explanatory variables and is normally distributed with mean zero and variance
σ2 .

Answers: the population error u is dependent on the explanatory variables and is normally distributed with mean equal to one and
variance σ2.

the population error u is independent of the explanatory variables and is normally distributed with mean equal to one and
variance σ.

the population error u is dependent on the explanatory variables and is normally distributed with mean zero and variance
σ.

the population error u is independent of the explanatory variables and is normally distributed with mean zero and variance
σ2 .

Question 7 1 out of 1 points

Which of the following statements is true of confidence intervals?

Selected Answer: Confidence intervals in a CLM provide a range of likely values for the population parameter.

Answers: Confidence intervals in a CLM are also referred to as point estimates.

Confidence intervals in a CLM provide a range of likely values for the population parameter.

Confidence intervals in a CLM do not depend on the degrees of freedom of a distribution.

Confidence intervals in a CLM can be truly estimated when heteroskedasticity is present.

Question 8 1 out of 1 points

Which of the following statements is true?

Selected
Answer: The upper bound of the confidence interval for a regression coefficient, say âj, is given by + [Critical value × standard
error ()].

Answers: When the standard error of an estimate increases, the confidence interval for the estimate narrows down.

Standard error of an estimate does not affect the confidence interval for the estimate.

The lower bound of the confidence interval for a regression coefficient, say âj, is given by - [standard error × ()].

The upper bound of the confidence interval for a regression coefficient, say âj, is given by + [Critical value × standard
error ()].

Question 9 1 out of 1 points

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10/18/23, 3:05 PM Review Test Submission: Online Quiz 5 (Chapter 6) – ...

The general t statistic can be written as:

Selected Answer:
t=

Answers:
t=

t=

t=

t=

Question 10 1 out of 1 points

A normal variable is standardized by:

Selected Answer: subtracting off its mean from it and dividing by its standard deviation.

Answers: subtracting off its mean from it and multiplying by its standard deviation.

adding its mean to it and multiplying by its standard deviation.

subtracting off its mean from it and dividing by its standard deviation.

adding its mean to it and dividing by its standard deviation.

Wednesday, 18 October 2023 7:05:50 PM AEDT

← OK

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