What is Econometrics?

Econometrics is the math behind economics. Econometrics combines economics and mathematical-statistical models to study economic data and create testable economic models.

Econometricians are the data scientists pioneering exploration into economic trends. Using an array of sophisticated statistical methods such as probability, inference, regression analysis, and frequency distributions, they help us better understand and predict our economic future.

After testing economic theories, these analysts compare results with real data and observations — providing invaluable insights for predicting upcoming financial changes that allow businesses to prepare accordingly.

If you're wondering about econometrics and the career possibilities for those with advanced econometrics skills, we've got your answers below.

Theoretical econometricians dive deep into the properties of existing economic models — including statistical tests and procedures — in search of ways to account for unknowns in a given model. Theoretical econometricians rely heavily on mathematics, theoretical statistics, and numerical methods to develop new statistical procedures that keep up with the peculiarities of economic data.

By utilizing these mechanisms, econometricians work to ensure that the new procedures are valid — or robust — and have the ability to draw correct inferences.

Econometrics is a subset of economics. Economists use economic models to explain observed, recurring relationships. For instance, economists know that as wealth increases, so does consumption. However, economic models don't provide specific details, such as how much wealth has to increase to drive increased consumption. Instead, the model merely provides a qualitative association between the two variables.

Econometric models attempt to quantify this qualitative relationship. For instance, a model explained in the paper "Stock Market Wealth and the Real Economy: A Local Labor Market Approach" found that for every dollar increase in stock market wealth, consumer spending increased by 2.8 cents. With this quantitative model, policymakers and economists can better predict what effects changes in the economy will have on society.¹

Econometricians use econometrics to answer questions such as:

• How will sales, economic growth, and inflation change in the next year?
• How will current conditions affect the sales of a particular product?
• How much will an advanced degree increase your income?
• How much will an increase in the interest rate slow economic growth?
• Will a salary increase lead to an increase in productivity?

Econometricians use the following methods to develop their models:

Propose a hypothesis

The hypothesis should explain the data under examination and explain the variables as well as the relationships and magnitudes between the variables. This theory can be drawn from existing economic theories to delve deeper into the variables and determine more specific magnitudes.

Define the conditions

The next step is to outline the parameters and determine which variables to examine. One variable will be the dependent variable, and the other will be the explanatory variable.

Choose a statistical model

The econometrician will choose a statistical model to propose a mathematical relationship between the variables. This is usually a linear model that's expected to show a change in the explanatory variable will produce the same change in the dependent variable. For example, in the paper above, a change in stock market wealth produced a change in consumer spending. This is a straight-line relationship.

Econometricians realize that they can't account for all of the influences on a dependent variable, so they include a "catchall" variable. The catchall variable accounts for the unexplained influences on the dependent variable.

Run the software

At this point, all of the variables and data are inputted into econometric software. The appropriate statistical procedures will be used to estimate the unknown parameter of the model based on the data.

Test the hypothesis

Finally, the hypothesis will be tested. Does it make good sense? Does it generate meaningful economic predictions? The statistical procedure probably needs some tweaking if the model suggests something that doesn't parallel accepted economic theory. If all has gone well in the previous steps, the result should be a tool that can help forecast the dependent variable. Policymakers can use this model to make financial decisions that help keep the economy running smoothly.

Although the software used to generate the mathematical relationship between variables is powerful, it's subject to the same limitations as any software. The quality of the model depends on the quality of the tools used to build it and the data it's given. You'll get unreliable and inaccurate results if a relevant variable is excluded from the model. Even if you have a good model, the parameter estimates are unlikely to equal the actual parameter values generated by the data. However, if the model is sound, the results should become more accurate as more data is generated.²

Theoretical econometrics studies existing statistical models and procedures to develop new models and procedures that can produce valid results. Theoretical econometricians use mathematics and theoretical statistics to prove that their new models can produce correct inferences.³

Applied econometrics is more concerned with using models to describe a quantitative relationship between variables in an economic model. Applied econometricians use the models developed by theoretical econometricians to conduct their research. Because they work more closely with the data and models, they're able to point out inconsistencies and issues in the models that theoretical econometricians may then work to resolve.

Critics of econometrics often accuse it of being too reliant on the interpretation of raw data, failing to link it to established theory, and ignoring causal mechanisms. They worry that without delving enough into the underlying mechanisms and developing theories that allow them to accurately explain our findings, econometricians can be susceptible to drawing false conclusions from raw data alone.

Though it should be noted that while econometricians mostly concern themselves with analyzing correlations, it is understood that correlation analysis yields insights into associations within datasets and this needs to be interpreted carefully - after all, association does not necessarily equate causation.

Therefore, it is imperative 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. Otherwise, econometricians run the risk of coming up with spurious correlations that may distort the reality of the findings.⁴

Econometricians typically have a master's degree in business analytics, mathematics, statistics, or economics. Coursework will likely include subjects such as:⁵

• Applied regressional analysis
• Data analytics
• Calculus
• Business statistics
• Price theory
• Financial engineering
• Statistics

The Online Master of Science in Business Analytics from Santa Clara University will prepare you to harness the power of big data and open the door to a lucrative and rewarding career. Whether you want to launch a career in econometrics or another field of business analytics, our program will give you the skills and knowledge you need.

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1. Retrieved on January 10, 2023, from https://www.nber.org/digest/aug19/new-estimates-stock-market-wealth-effect
2. Retrieved on January 10, 2023, from https://home.uchicago.edu/amshaikh/webfiles/testingreview.pdf
3. Retrieved on January 10, 2023, from https://www.imf.org/external/pubs/ft/fandd/basics/econometric.htm
4. Retrieved on January 10, 2023, from https://www.investopedia.com/terms/e/econometrics.asp
5. Retrieved on January 10, 2023, from https://www.aeaweb.org/resources/students/grad-prep/math-training#:~:text=programs%20expect%20applicants%20to%20have,algebra%2C%20and%20basic%20probability%20theory.