Semiparametric Bayesian Estimation of Random Coefficients Discrete Choice Models

Sylvie Tchumtchoua () and Dipak Dey ()
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Sylvie Tchumtchoua: University of Connecticut
Dipak Dey: University of Connecticut

No 102, Food Marketing Policy Center Research Reports from University of Connecticut, Department of Agricultural and Resource Economics, Charles J. Zwick Center for Food and Resource Policy

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: Dependent Dirichlet process; Discrete choice models; Heterogeneity; Markov chain; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 C14 C25 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2007-10
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Persistent link: https://EconPapers.repec.org/RePEc:zwi:fpcrep:102

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