Econometric Modeling: Go Beyond the Spreadsheet to Unlock Business Insights

Key takeaways 

Econometric models help explain relationships in economic data and guide decisions across business, policy, and research. Here are some important facts to know:

• Linear regression models are commonly used, appearing in 26 of the 100 most cited American Economic Review papers from 2015-2019 [1]. 

• Combining different data types gives econometricians more power to isolate causal relationships and control for confounding variables.

• You can select the appropriate econometric model by thinking about the nature of your outcome variable (continuous vs. binary), whether your analysis involves time trends, and whether multiple variable types are present in your data set. 

Learn more about selecting an econometric model for your data and how to build your own model to improve decision making. If you’re ready to build relevant data analysis skills, consider enrolling in the Accounting Data Analytics Specialization from the University of Illinois at Urbana-Champaign.

Types of econometric models

Econometric models provide different ways to analyze relationships between data and predict future economic trends. The right choice of model depends on the type of question you want to answer. You can choose between several types of econometric models, depending on your question at hand.

Linear regression models

If you want to estimate how one or more variables influence a continuous outcome, you might choose a linear regression model. For example, your business might want to use regression to measure how spending on advertisements affects sales revenue, or how the number of advertisements affects website traffic. 

Linear regression models are widely used because they’re considered to be more straightforward and easily interpretable than many other types of models. In fact, in the American Economic Review (2015-2019), 26 percent of the 100 top-cited papers used linear regression approaches [1]. By using linear regression, you can gain a clearer picture of the strength and direction of variable relationships, allowing you to make more informed business decisions.

Logit and probit models

If you have a binary outcome, meaning two possible results, you might opt for a logit or probit model. These models help you predict the likelihood of an event occurring based on your explanatory variables. 

For example, you might want to predict whether a customer will make a certain purchase (buy/not buy) based on the type of advertisement they see, or whether customers are likely to return your product (yes/no) based on their buying behavior. 

Time-series models

If you collect data over time, time-series models can help you forecast trends, such as stock prices or seasonal fluctuations in consumer demand. You can capture patterns over months or years, helping your organization prepare for future needs. 

Panel data models

If you want to combine cross-sectional and time-series data, you can use panel data models to follow multiple entries across several periods. For example, you might want to know how changes in family size affect spending patterns. You can follow the same household over multiple years and see variation over time. This can help you develop richer insights by looking both between and within groups over time.

Econometric model examples and use cases

Your organization can use econometric modeling to connect data to decision-making in ways that go beyond surface-level observations. At a basic level, these models allow you to test whether hypotheses hold true in practice, forecast business outcomes, and evaluate how effective your policies and strategies are. 

What your econometric models will look like in practice depends on the sector you’re in. For example:

• If you’re in retail, you might use time-series models to anticipate seasonal fluctuations in sales by analyzing historical data on consumer behavior and the effects of promotional events.

• If you’re part of a government agency, you might use econometric models to look at how much investment into farming is needed to raise farmer incomes to a sustainable level. 

• If you’re in health care, you might use econometric models to predict how patient outcomes might improve in response to new interventions.

While different in model use, each of these econometric models provides a structured and evidence-based way for you to move past direct observation to detect more meaningful relationships between variables. This can help you see not only what is happening, but why.

What are the four types of data in econometric modeling?

You’ll typically see four types of data in econometric data: cross-sectional, time-series, panel, and pooled data. Cross-sectional data captures information at one time point, like in a survey. Time-series data follows trends across periods of days, months, or years. Panel data tracks the same entities over multiple periods, which allows you to observe both individual differences and changes over time. Pooled data occurs when you collect random samples of cross-sectional data at different points in time and group them together. 

Who uses econometric modeling? 

Companies that want to use data to inform decision-making use econometric modeling to guide their strategy and reduce uncertainty. In business, these models support forecasting demand, setting prices, measuring policy impact, and assessing risk. By basing decisions on data, your company can gain a competitive advantage and respond more effectively to market conditions.

Professionals working in this space, known as econometrists, typically work for private companies, government agencies, or academic institutions. They hold titles such as financial analyst, economist, data scientist, and market researcher, though their exact responsibilities differ depending on their industry.

Pros and cons of econometric models 

Deciding whether using econometric models is right for your business depends on your priorities and available resources. In making this decision, considering the advantages and disadvantages can help you make an informed decision.

Advantages 

• Forecasting abilities: Econometric models allow your organization to predict future trends using historical data and movements over time. 

• Testing hypotheses: You can test your theories and assess how strongly one variable may predict another.

• Data-driven decision support: With econometrics models, you use direct evidence to make decisions rather than relying on your intuition to guide you. This helps with unbiased decision-making and accountability.

• Capture complexity: You can capture both individual and contextual factors in your econometric models.

Disadvantages

• Lack of transparency: Transparency has historically been a concern in some econometric models. If you don’t clearly document each assumption you made and why, it can be difficult for people to fully understand and interpret your findings. 

• Model uncertainty: All model specifications involve some degree of uncertainty because they depend on the data and methods used. Checking stability under different conditions helps ensure your model is useful in practice.

• Data limitations: Econometric models are only as accurate as the data you have. If you have missing, misleading, or noisy data, the models may not be as useful.

What is a dynamic model in econometrics?

In econometrics, a dynamic model is a model that explicitly accounts for variable changes over time and how past values influence present outcomes. Instead of only looking at the current relationship, dynamic models incorporate lagged variables, which are past values of the dependent or independent variables. For example, you could forecast economic growth for both short-term dynamics and long-term trends, such as the lagged effects of energy price shocks or stock market crashes.

How to build​ an econometric model

To build an econometric model, you can follow a set of steps to organize your flow of model specification and development. As a baseline, consider the following steps:

1. State your theory or hypothesis. From this, identify your explanatory and outcome variables. 

2. Create your model specifications. Based on your variables, determine which type of model is most appropriate to model the relationship. 

3. Estimate your outcome. Run your model and estimate the unknown coefficients of your model using your data. 

4. Check whether the outcome makes sense. Consider whether the outcome is plausible and whether it aligns with current theories in the field. Not only should your estimates be statistically sound, but they should be economically sensible. 

Explore free resources for econometrics 

Learn more about econometrics and related fields by subscribing to Career Chat, where you can explore career advice, skill insights, and more. Or check out the following resources to keep learning more:

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• Watch on YouTube: Understanding economic policymaking 

• Bookmark this page: Data Analysis Terms & Definitions

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