The Ins and Outs of Using Dynamic Regression Models for Forecasting

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1 The Ins and Outs of Using Dynamic Regression Models for Forecasting Presented by Eric Stellwagen Vice President & Cofounder Business Forecast Systems, Inc. Business Forecast Systems, Inc. 68 Leonard Street Belmont, MA USA (617)

2 Eric Stellwagen Vice President, CEO & Cofounder of Business Forecast Systems, Inc. Coauthor of Forecast Pro product line. Over 25 years in forecasting. Currently serving on the board of directors of the International Institute of Forecasters and on the practitioner advisory board of Foresight: The International Journal of Applied Forecasting.

3 What We ll Cover Introductions Overview Example Building the Model Summary Q&A

4 Pros: s Dynamic Regression Allows for the introduction of explanatory variables. Lends insight into relationships between variables. Allows for what if scenarios. Can exploit leading indicators.

5 Dynamic Regression Cons: s Is not automatic, requires considerable expertise and time. Even if the regression model is good, difficulties in forecasting the explanatory variables will result in poor forecasts of the dependent variable. Data requirements are large.

6 The Classical Regression Model Sales = b Advertising + Constant + Error Y t = bx t + c + e t Y t : Dependent variable X t : Independent (explanatory) variable b : Regression coefficient c : Constant term e t : Error term

7 Independent Variables Internal Variables Prices Promotion External Variables Weather Economy Competition Demographics

8 What s the Work Involved? Collect all necessary independent variables including forecasts. Build the model.

9 Example

10 Establishing a Baseline Model It is often useful to create a time series model for the dependent variable prior to building the dynamic regression model. This is quick and easy and provides a baseline.

11 Model Identification Building regression models is generally an iterative procedure. You start with an initial model and experiment with adding or removing variables, lags and dynamic terms. Intuition, hypothesis tests and other diagnostics can guide the process. Automatic algorithms do not perform well for dynamic regression modeling.

12 Regression: Model Validation Model must make economic sense (e.g., coefficients must be the proper sign). Coefficients should be significant (t-statistics probablility should ideally be.99 or above). Errors should not be autocorrelated (Ljung-Box probability less than.95, error autocorrelation function unpatterned).

13 Dynamic Regression Dynamic regression models combine time series elements with classical regression. This can take different forms, including: Lagged dependent variables Cochrane-Orcutt terms

14 Summary Should be considered when there are important explanatory variables. Lends insight into relationships between variables and allows for what if scenarios. Requires forecasts for the explanatory variables unless they are leading indicators. Building the model is an iterative procedure that requires some knowledge of the data and the modelbuilding process.

15 Forecasting Seminars and Workshops BFS offers forecasting seminars and product training workshops. Public, on-site, and remote-based (via WebEx) classes are available. Learn more at

16 Forecast Pro Examples from today s Webinar used Forecast Pro. To learn more about Forecast Pro: Request a live WebEx demo for your team (submit your request as a question right now) Visit Call us at

17 Questions?

18 Thank you for attending!