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Econometrics: Definition, Models, and Methods

By

ADAM HAYES

Updated May 10, 2025

Reviewed by
KHADIJA KHARTIT

Fact checked by

YARILET PEREZ

DEFINITION
Econometrics involves applying statistical methods to uncover patterns and build
theories in economics and finance.
What Is Econometrics?
Econometrics is the use of statistical and mathematical models to construct theoretical
frameworks or verify prior hypotheses in economics and to forecast future trends from historical
data. It subjects real-world data to statistical trials and then compares the results against the
theory being tested.

Depending on whether you are interested in testing an existing theory or in using existing data to
develop a new hypothesis, econometrics can be subdivided into two major categories: theoretical
and applied. Those who routinely engage in this practice are commonly known as
econometricians.

KEY TAKEAWAYS

 Econometrics relies on techniques such as regression models and null hypothesis testing.
 Econometrics can be used to forecast future economic or financial trends.
 Some economists have criticized the field of econometrics for prioritizing statistical models over
economic reasoning.
Econometrics is a valuable tool for businesses, governments, researchers, and financial
institutions.

Investopedia / Michela Buttignol

Understanding Econometrics
Econometrics analyzes data using statistical methods to test or develop economic theory.

These methods rely on statistical inferences to quantify and analyze economic theories by
leveraging tools such as frequency distributions, probability, and probability distributions,
statistical inference, correlation analysis, simple and multiple regression analysis, simultaneous
equations models, and time series methods.

Econometrics was pioneered by Lawrence Klein, Ragnar Frisch, and Simon Kuznets. All three
won the Nobel Prize in economics for their contributions.123 Today, it is used by academics as
well as Wall Street traders and analysts.

An example of the application of econometrics is to study the income effect using observable
data. An economist may hypothesize that as a family increases their income, their spending will
also increase.

If the data shows that such an association is present, a regression analysis can then be conducted
to understand the strength of the relationship between income and consumption and whether or
not that relationship is statistically significant—that is, whether the change in consumption could
be due to chance alone.

Methods of Econometrics
The first step to econometric methodology is to obtain and analyze a set of data and define a
specific hypothesis that explains the nature and shape of the set. This data may be, for example,
historical prices for a stock index, observations collected from a survey of consumer finances, or
unemployment and inflation rates in various countries.

If you are interested in the relationship between the annual price change of the S&P 500 and the
unemployment rate, you'd collect both sets of data. Then, you might test the idea that higher
unemployment leads to lower stock market prices. In this example, stock market prices would be
the dependent variable, and the unemployment rate is the independent or explanatory variable.

The most common relationship is linear, meaning that any change in the explanatory variable
will have a positive correlation with the dependent variable. This relationship could be explored
with a simple regression model, which amounts to generating a best-fit line between the two sets
of data and then testing to see how far each data point is, on average, from that line.

Note that you can have several explanatory variables in your analysis—for example, changes to
GDP and inflation in addition to unemployment in explaining stock market prices. When more
than one explanatory variable is used, it is referred to as multiple linear regression. This is the
most commonly used tool in econometrics.
WARNING
Some economists, including John Maynard Keynes, have criticized econometricians
for their reliance on statistical correlations rather than sound reasoning.
Different Regression Models
Several regression models are optimized depending on the nature of the data being analyzed and
the type of question being asked.

The most common example is the ordinary least squares (OLS) regression, which can be
conducted on several types of cross-sectional or time-series data. If you're interested in a binary
(yes-no) outcome—for instance, how likely you are to be fired from a job based on your
productivity—you might use a logistic regression or a probit model.

Today's econometricians have hundreds of models at their disposal.

Econometrics is now conducted using statistical analysis software packages designed for these
purposes, such as STATA, SPSS, or R. These software packages can also easily test for
statistical significance to determine the likelihood that correlations might arise by chance.

R-squared, t-tests, p-values, and null-hypothesis testing are all methods used by econometricians
to evaluate the validity of their model results.

Limitations of Econometrics
Econometrics is sometimes criticized for relying too heavily on the interpretation of data without
linking it to established economic theory or looking for causal mechanisms. It is crucial that the
findings revealed in the data can be adequately explained by a theory, even if that means
developing a new theory of the underlying processes.

Regression analysis also does not prove causation. An association between two data sets may be
spurious.

For example, drowning deaths in swimming pools increase with growth in gross domestic
product (GDP). Does a growing economy cause people to drown? It's more likely that more
people buy pools when the economy is booming.

Econometrics is largely concerned with correlation analysis, and it is important to remember that
correlation does not equal causation.

What Are Estimators in Econometrics?

An estimator is a statistic based on a sample that is used to extrapolate a fact or measurement for
a larger population. Estimators are frequently used in situations where it is not practical to
measure the entire population.

For example, it is not possible to measure the exact employment rate at any specific time, but it
is possible to estimate unemployment based on a random sampling of the population.
What Is Autocorrelation in Econometrics?
Autocorrelation measures the relationships between a single variable at different time periods.
For this reason, it is sometimes called lagged correlation or serial correlation, since it is used to
measure how the past value of a certain variable might predict future values of the same variable.

Autocorrelation is a useful tool for traders, especially in technical analysis.

What Is Endogeneity in Econometrics?

An endogenous variable is a variable that is influenced by changes in another variable.

Due to the complexity of economic systems, it is difficult to determine all of the subtle
relationships between different factors, and some variables may be partially endogenous and
partially exogenous.

In econometric studies, the researchers must be careful to account for the possibility that
the error term may be partially correlated with other variables.

The Bottom Line

Econometrics is a popular discipline that integrates statistical tools and modeling for economic
data. It is frequently used by policymakers to forecast the result of policy changes.

As with other statistical tools, there are many possibilities for error when econometric tools are
used carelessly. Econometricians must be careful to justify their conclusions with sound
reasoning as well as statistical inferences.

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