Bayesian Estimation of Random-Coefficients Choice Models Using Aggregate Data

Journal of Applied Econometrics, Forthcoming

44 Pages Posted: 10 Apr 2005 Last revised: 5 Apr 2012

See all articles by Andres Musalem

Andres Musalem

Universidad de Chile

Eric Bradlow

University of Pennsylvania - Marketing Department

Jagmohan S. Raju

University of Pennsylvania - Marketing Department

Date Written: February 2006

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 Classification: C11, C15, C23

Suggested Citation

Musalem, Andres and Bradlow, Eric and Raju, Jagmohan S., Bayesian Estimation of Random-Coefficients Choice Models Using Aggregate Data (February 2006). Journal of Applied Econometrics, Forthcoming. Available at SSRN: https://ssrn.com/abstract=687153 or http://dx.doi.org/10.2139/ssrn.687153

Andres Musalem (Contact Author)

Universidad de Chile ( email )

Republica 701
Santiago
Chile

HOME PAGE: http://www.dii.uchile.cl/~amusalem

Eric Bradlow

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States
215-898-8255 (Phone)

Jagmohan S. Raju

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States
215-898-1114 (Phone)
215-898-2534 (Fax)

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