Bayesian analysis of random coefficient logit models using aggregate data

Renna Jiang

doi:10.1016/j.jeconom.2008.12.010

Return

Bayesian analysis of random coefficient logit models using aggregate data

2009-04-01

PRE

Renna Jiang

Puneet Manchanda

Peter E. Rossi *

DOI：10.1016/j.jeconom.2008.12.010

Original

Original request for help

Save

Abstract

En 中文

We present a Bayesian approach for analyzing aggregate level sales data in a market with differentiated products. We consider the aggregate share model proposed by Berry et al. [Berry, Steven, Levinsohn, James, Pakes,Ariel, 1995. Automobile prices in market equilibrium. Econometrica. 63 (4),841-890], which introduces a common demand shock into an aggregated random coefficient logit model. A full likelihood approach is possible with a specification of the distribution of the common demand shock. We introduce a reparameterization of the covariance matrix to improve the performance of the random walk Metropolis for covariance parameters. We illustrate the usefulness of our approach with both actual and simulated data. Sampling experiments show that our approach performs well relative to the GMM estimator even in the presence of a mis-specified shock distribution. We view our approach as useful for those who are willing to trade off one additional distributional assumption for increased efficiency in estimation. (C) 2008 Elsevier B.V. All rights reserved.

Keywords:

Random coefficient logit

Aggregate share models

Bayesian analysis

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.