Response Function Estimation Using the Equity Estimator

Overall, equity outperforms the other three estimators on criteria such as estimated bias, variance, and face validity of the estimates, and some managerial implications for resource allocation in the pharmaceutical industry are presented.

Abstract

Multicollinearity often hampers the estimation of the “independent” effects of the marketing mix variables in sales response models. In a previous study, the authors recommended the use of the equity estimator for estimating linear models in the presence of multicollinearity. In this article, they evaluate the performance of equity, ridge, OLS, and principal components estimators in estimating response functions for 36 pharmaceutical products. Overall, equity outperforms the other three estimators on criteria such as estimated bias, variance, and face validity of the estimates. The four estimators have similar levels of predictive accuracy. The authors also present some managerial implications for resource allocation in the pharmaceutical industry.