Multicollinearity in marketing models: Diagnostics and remedial measures

The problem of multicollinearity in linear models is reviewed. Diagnostic measures for detection, analysis of the effects, and localization of multicollinearity are presented. It is recommended that OLS estimates should not be used without a proper diagnostic. The traditional remedial measures, i.e., omission of variables from the model and principal component regression, are critically discussed along with more recent methods. Ridge regression is presented, and several methods for selecting the biasing parameter in ridge regression are introduced. The procedures are illustrated with data taken from the marketing literature and evaluated for their potential usefulness in handling multicollinearity.