Forecasting 101: Dynamic Regression: What Is It and When Should I Use It?

Dynamic regression models allow you to incorporate causal factors such as prices, promotions and economic indicators into your forecasts. The models combine standard OLS regression (as offered in Excel) with the ability to use dynamic terms to capture trend, seasonality and time-phased relationships between variables. The result is a model that will forecast more accurately than straight time series approaches when explanatory variables are driving the demand for your products or services and certain other conditions are met.

A well-specified dynamic regression model lends considerable insight into relationships between variables and allows for “what if” scenarios. For instance, let’s say that your dynamic regression model includes price as an explanatory variable. By quantifying the relationship between sales and price, the model allows you to create forecasts under varying price scenarios. “What if we raise the price?” “What if we lower it?” Generating these alternative forecasts can help you to determine an effective pricing strategy.

The “what if” analysis described above hints at the biggest drawback to using dynamic regression. A well-specified dynamic regression model captures the relationship between the dependent variable (the one you wish to forecast) and one or more independent variables. In order to generate a forecast, you must supply forecasts for your independent variables. If these independent variables are under your control (e.g., prices, promotions, etc.) or if they are leading indicators, this may not be a big issue. If, however, your independent variables are not under your control (e.g., weather, interest rates, price of materials, competitive offerings, etc.) then you need to keep in mind that poor forecasts for the independent variables will lead to poor forecasts for the dependent variable.

Forecast Pro offers dynamic regression, where explanatory variables and dynamic terms that capture trend, seasonality and time-phased relationships between variables are combined. The Forecast Pro expert system’s self-interpreting hypothesis tests and other diagnostics help guide you through the model building process.

The Model Building Process
Most of the forecasting methods in Forecast Pro can be highly automated, where the Forecast Pro expert system performs various statistical tests and then selects and builds the final model. Regression is a bit different. It is the one method in Forecast Pro where knowledge of the technique and experience building the models is quite useful. (BFS offers several excellent opportunities to learn more about the theory and application of dynamic regression. Please see the links at the end of this article.) Building a dynamic regression model is generally an iterative procedure, whereby you begin with an initial model and experiment with adding or removing independent variables and dynamic terms until you arrive upon an acceptable model. Forecast Pro provides a complete range of self-interpreting hypothesis tests and other diagnostics to help guide you through the process.

Conclusion
Dynamic regression is a powerful forecasting technique that allows you to incorporate the impact of explanatory variables into your forecasts. In addition to generating a forecast, a well-specified model can provide considerable insight into relationships between variables and allow for “what if” modeling. If demand for your products or services is driven by causal variables and you can obtain historical data and reliable forecasts for these variables, you should consider using dynamic regression.

The Forecasting Seminar track at Forecasting Summit offers instruction in business forecasting methods, including time series and regression modeling. Click here to learn more.

About the author:
Eric Stellwagen is Vice President and co-founder of Business Forecast Systems, Inc. (BFS) and co-author of the Forecast Pro software product line. He consults widely in the area of practical business forecasting—spending 20-30 days a year presenting workshops on the subject—and frequently addresses professional groups such as the University of Tennessee’s Sales Forecasting Management Forum, APICS and the Institute for Business Forecasting. Recognized as a leading expert in the field, he has worked with numerous firms including Coca-Cola, Procter & Gamble, Merck, Blue Cross Blue Shield, Nabisco, Owens-Corning and Verizon, and is currently serving on the board of directors of the International Institute of Forecasters (IIF).