Dynamic Regression is a regression model that includes lagged values of explanatory variables or of dependent variables or both. The relationship between the forecast variable and the explanatory variable is modeled using a transfer function. A dynamic regression model can predict what will happen if the explanatory variable changes.
The Dynamic Regression model is similar to Regression Analysis, but it is believed to produce more realistic results, because it emphasizes the ripple effects the input variables can have on the dependent variable. For example, a price change made today might influence sales volumes in a variety of ways for many future periods.
Source: Jeffrey Wooldridge – Introductory Econometrics: A Modern Approach
Source: Russell Davidson, James G. Mackinnon – Econometric Theory and Methods
Source: Alan Pankratz – Forecasting with Dynamic Regression ModelsBusiness frameworks like Dynamic Regression are invaluable to evaluating and analyzing various business problems. You can download business frameworks developed by management consultants and other business professionals at Flevy here.