INTRODUCTION TO ECONOMETRICS

• Econometrics- combining two Greek words, “oikonomia”

(Economics) and “metron” (measure).
• Literal meaning ‘measurement in Economics’.
• Branch of economics, which is a combination of mathematics,
statistics and economic theory or the application of mathematics
and statistical methods to the analysis of economic data.
• Econometrics is statistical and mathematical analysis of
economic relationships; science of empirically verifying
economic theories; set of tools used for forecasting future values
of economic variables and science of making quantitative policy
recommendations in government.
• The Econometrics has recognised as a branch of Economics only
in the 1930s with the foundation of ‘Econometric Society’ by
Ragnar Frisch and Irving Fisher.
• The term Econometrics have been first used by Powel Ciompa as
early as 1910, but Ragnar Frisch is credited for coining the term
and establishing it as a subject.
• In history of Econometrics, R J Epstien viewed Henry Moore
(1896-1958) as father of modern Econometrics because of his
attempt in 1911 to provide statistical evidence for marginal
productivity theory.
Classification of Econometrics

• Theoretical econometrics is concerned with the development of

appropriate methods for measuring economic relationships
specified by econometric models.
 • In applied econometrics we use the tools of theoretical
econometrics to study some special field(s) of economics and
business, such as the production function, investment function,
demand and supply functions, portfolio theory, etc.
Uses of Econometrics
• In testing economic theories
• In formulation and evaluation of economic policy
• Prediction of macroeconomic variables
• In finding macroeconomic relationships
• Microeconomic relationship
• Finance
• In private and public sector decision making.
Econometrics Versus Mathematical economics
• Econometrics and Mathematical Economics are two closely
related but distinct fields of study within economics.
• Econometrics is a branch of economics that uses statistical
methods and mathematical models to analyze economic data and
test economic theories. It is concerned with the measurement of
economic relationships and the testing of hypotheses about those
relationships. Econometrics involves the use of statistical
methods to estimate the parameters of economic models, test the
validity of those models, and predict the outcomes of economic
events.
• Mathematical Economics, on the other hand, is a branch of
economics that uses mathematical methods to model economic
systems and solve economic problems. It is concerned with the
development and application of mathematical models and
computational techniques to study economic phenomena.
Mathematical Economics involves the use of mathematical
techniques to derive analytical results and solve economic
problems.
• The main difference between econometrics and mathematical
economics is that econometrics focuses on empirical analysis of
economic data, while mathematical economics focuses on
 theoretical modelling and analysis. Econometrics relies heavily
on statistical analysis, while mathematical economics relies on
mathematical analysis.
Limitations of Econometrics
• It is concerned only with quantifiable phenomena like prices,
production, employment etc. It throws very little light on
qualitative problems.
• All the econometric analysis is based on data availability. The
available data may be insufficient and inaccurate.
• Predictions are made through sampling methods. Therefore, the
limitations of the sampling method are also became the
limitations of econometrics.
• The statistical methods used in econometrics are based on certain
assumptions, which are not true with economic data.
• Econometric methods are time consuming, tedious and complex.
It requires a sound knowledge of mathematics and statistics.
 THE ASSUMPTIONS UNDERLYING THE
METHOD OF OLS OR THE CLASSICAL
LINEAR REGRESSION MODEL
• The Gaussian, standard, or classical linear regression model
(CLRM), which is the cornerstone of most econometric theory,
makes 10 assumptions.
• It is classical in the sense that it was developed firstly by Gauss
in 1821 and since then has served as a norm or a standard against
which may be compared the regression models that do not satisfy
the Gaussian assumptions.
1. The Model is Linear in Parameter
• Yi = β1 + β2Xi +ui
• The parameter β1 and β2 should appear with power one and
it is not multiplied or divided with any other parameters.
• Linearity implies that one unit change in X has the same
effect on Y irrespective of the initial value of X.
• OLS estimation is a method to deal with linear estimation.
2. The value of explanatory variable X need to vary in the
sample and fixed in the repeated sample.
3. Zero Mean for Disturbances
• The conditional mean of the random error terms ui for
any given value of Xi is equal to zero.
• The expected value of the disturbance term in any
observation should be zero. Sometimes it will be positive,
sometimes it will be negative, but it should not have a
systematic tendency in either direction.
• E(ui/Xi) = 0
4. The Disturbance Term is Homoscedastic
• Homoscedastic means equal variance or equal spread.
• The word comes from the Greek verb skedanime, which
means to disperse or scatter.
• The conditional population variances of the random error
terms ui corresponding to each population value of Xi are
equal to the same finite positive constant, σ2 .
• Once we have generated the sample, the disturbance term
will be greater in some observation and smaller in others,
but there should not be any priori reason for it to be more
erratic in some observations than in others.

5. Non-autocorrelated Errors
• The disturbance terms are not subject to autocorrelation,
there should not be any systematic association between
the values of the disturbance term in any two
observations.
• Consider the random error terms ui and uj (i≠j)
corresponding to two different population values Xi and
Xj, where Xi ≠ Xj. This assumption states that ui and uj
have zero conditional covariance.
• For example, just because of the disturbance term is
large and positive in one observation, there should be no
tendency for it to be large and positive in the next and
vice versa.
• The values of the disturbance term should be absolutely
independent of one another.
• This means that for a given Xi, the deviation of any two
Y values from their mean value do not exhibit any
systematic pattern.
6. 6) Zero Covariance Between ui and Xi
• The disturbance term u and explanatory variable X are
uncorrelated.
• We assumed that X and u have separate influence on Y.
But if X and u are correlated, it is not possible to assess
their individual effects on Y.
• Thus, if X and u are positively correlated, X increases
when u increases and it decreases when u decreases.
Similarly, if X and u are negatively correlated, X
increases when u decreases and it decreases when u
increases. In either case, it is difficult to isolate the
influence of X and u on Y.
7. The Number of Observations must be Greater than Number
of Parameters to be Estimated.
• The number of observations ‘n’ must be greater than
the number of explanatory variables.
• Suppose we have only one pair of observations on Y
and X.
• From this single observation there is no way to
estimate the two unknowns, β1 and β2.
• We need at least two pairs of observations to estimate
the two unknowns.
8. Variability in X values
• The X values in a given sample must not all be the
same.
• If there is very little variation in family income, we
will not be able to explain much of the variation in the
consumption expenditure.
• Variation in both Y and X is essential to use
regression analysis as a research tool. In short, the
variables must vary.
9. Regression Model is Correctly Specified
• The regression model is correctly specified.
• There is no specification bias or error in the model used
in empirical analysis.
• There is no such errors like omitting important variables
from the model, or by choosing the wrong functional
form, or by making wrong stochastic assumptions about
the variables of the model.
10. No Perfect Multicollinearity
• There is no perfect linear relationship among the
explanatory variables in a linear regression model.