Estimating Models of Seasonal Market Response Using Dummy Variables

Abstract

Researchers often suggest the usefulness of regression models of market response as aids to market decision making. Commonly, models which involve monthly, bimonthly, or quarterly data explicitly account for seasonal variations by the use of dummy variables. This note critically examines the use of dummy variables in seasonal models. The results of the examination indicate that although the commonly used seasonal shift model (seasonal dummy variables which influence only the intercept term of the model) may be successful in explaining the variance in market response, it may not explain it in a manner indicative of market behavior, and therefore may result in misleading implications for marketing decisions. Alternate seasonal specifications are discussed and statistical methods for selecting the appropriate model specification are considered.