Semiparametric Bayesian estimation of random coefficients discrete choice models

Abstract

Heterogeneity in choice models is typically assumed to have a normal distribution in both Bayesian and classical setups. In this paper, we propose a semiparametric Bayesian framework for the analysis of random coefficients discrete choice models that can be applied to both individual as well as aggregate data. Heterogeneity is modeled using a Dirichlet process, which varies with consumers’ characteristics through covariates. We develop a Markov Chain Monte Carlo algorithm for fitting such model, and illustrate the methodology using two different datasets: a household-level panel dataset of peanut butter purchases, and supermarket chain-level data for 31 ready-to-eat breakfast cereal brands.

Citation

Tchumtchoua, S. and Dey, D.K. (2008), "Semiparametric Bayesian estimation of random coefficients discrete choice models", Chib, S., Griffiths, W., Koop, G. and Terrell, D. (Ed.) Bayesian Econometrics (Advances in Econometrics, Vol. 23), Emerald Group Publishing Limited, Leeds, pp. 275-307. https://doi.org/10.1016/S0731-9053(08)23009-4