Semiparametric Bayesian Estimation of Random Coefficients Discrete Choice Models

Sylvie Tchumtchoua and Dipak Dey

No 149208, Research Reports from University of Connecticut, Food Marketing Policy Center

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 prior 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 cereals brands.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 49
Date: 2007-10
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Persistent link: https://EconPapers.repec.org/RePEc:ags:uconnr:149208

DOI: 10.22004/ag.econ.149208

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