Bayesian Estimation of Random-Coefficients Choice Models Using Aggregate Data

Abstract:     
This article discusses the use of Bayesian methods for estimating logit demand models using aggregate data, i.e. information solely on how many consumers chose each product. We analyze two different demand systems: independent samples and consumer panel. Under the first system, there is a different and independent random sample of N consumers in each period and each consumer makes only a single purchase decision. Under the second system, the same N consumers make a purchase decision in each of T periods. The proposed methods are illustrated using simulated and real data, and managerial insights available via data augmentation are discussed in detail.

Keywords: Discrete Choice Models, Data Augmentation, Markov Chain Monte Carlo Simulation, Random Coefficients

JEL Classifications: C11, C15, C23

Working Paper Series