Statistics Papers

Document Type

Journal Article

Date of this Version

4-2009

Publication Source

Journal of Applied Econometrics

Volume

24

Issue

3

Start Page

490

Last Page

516

DOI

10.1002/jae.1060

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, Aggregate Data, Bayesian Methods, Markov Chain Monte Carlo Simulation, Data Augmentation, Random Coefficients

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Date Posted: 27 November 2017

This document has been peer reviewed.