Estimating Price Elasticities with Theory-Based Priors

Abstract

The authors show how price elasticity estimates can be improved in demand systems that involve multiple brands and stores. They treat these demand models in a hierarchical Bayesian framework. Unlike in more standard Bayesian hierarchical treatments, the authors use prior information based on the restrictions imposed by additive utility models. In an additive utility model approach, price elasticities are driven by a general substitution parameter as well as brand-specific expenditure elasticities. The authors employ a differential shrinkage approach in which price elasticities are held closely to the restrictions of the additive utility theory and store-to-store variation is accommodated through differences in expenditure elasticities. Application of these new methods to simulated and real store scanner data shows significant improvements over existing Bayesian and non–Bayesian methods.