# Econometrics: Definition, Models, and Methods

> **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](https://www.investopedia.com/terms/h/hypothesistesting.asp) in economics and to [forecast](https://www.investopedia.com/terms/f/forecasting.asp) 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](https://www.investopedia.com/thmb/c5FOPh5dNEv1Rg7CB8HnWy0FtdA=/1500x0/filters:no_upscale():max_bytes(150000):strip_icc()/Econometrics_4-3-v2-resized-bbc43da03e7d4a94827965767d7b96a8.jpg)

*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](https://www.investopedia.com/terms/f/frequencydistribution.asp), probability, and [probability distributions](https://www.investopedia.com/terms/p/probabilitydistribution.asp), statistical inference, correlation analysis, simple and multiple regression analysis, simultaneous equations models, and time series methods.

Econometrics was pioneered by [Lawrence Klein](https://www.investopedia.com/terms/l/lawrence-klein.asp), Ragnar Frisch, and [Simon Kuznets](https://www.investopedia.com/terms/s/simon-kuznets.asp). All three won the Nobel Prize in economics for their contributions. 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](https://www.investopedia.com/terms/i/incomeeffect.asp) 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](https://www.investopedia.com/terms/r/regression.asp) 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](https://www.investopedia.com/terms/s/sp500.asp) 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](https://www.investopedia.com/terms/m/mlr.asp). 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](https://www.investopedia.com/terms/l/least-squares-method.asp) (OLS) regression, which can be conducted on several types of [cross-sectional](https://www.investopedia.com/terms/c/cross_sectional_analysis.asp) 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](https://www.investopedia.com/terms/r/r-squared.asp), [t-tests](https://www.investopedia.com/terms/t/t-test.asp), [p-values](https://www.investopedia.com/terms/p/p-value.asp), and [null-hypothesis](https://www.investopedia.com/terms/n/null_hypothesis.asp) 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](https://www.investopedia.com/terms/a/autocorrelation.asp) 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](https://www.investopedia.com/terms/e/endogenous-variable.asp) 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](https://www.investopedia.com/terms/e/errorterm.asp) 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.

> **Article Sources**
>
> 1.  The Nobel Prize. "[Simon Kuznets](https://www.nobelprize.org/prizes/economic-sciences/1971/kuznets/facts/)."
>     
> 2.  The Nobel Prize. "[Ragnar Frisch](https://www.nobelprize.org/prizes/economic-sciences/1969/frisch/facts/)."
>     
> 3.  The Nobel Prize. "[Lawrence R. Klein](https://www.nobelprize.org/prizes/economic-sciences/1980/klein/facts/)."