Dynamic Regression

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Of course nobody can really see the future. But modern statistical methods, econometric models and business intelligence software can help businesses to make forecasts, and they can also help them to estimate what is going to happen in the future. 

What is Dynamic Regression?

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.

Book: Jeffrey Wooldridge - Introductory Econometrics: A Modern Approach

Book: Russell Davidson, James G. Mackinnon - Econometric Theory and Methods

Book: Alan Pankratz - Forecasting with Dynamic Regression Models

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Compare with: Regression Analysis  |  Exponential Smoothing  |  ARIMA  |  Analytical CRM

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