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Semiparametric Bayesian Estimation of Random Coefficients Discrete Choice Models

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  • Tchumtchoua, Sylvie
  • Dey, Dipak

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.

Suggested Citation

  • Tchumtchoua, Sylvie & Dey, Dipak, 2007. "Semiparametric Bayesian Estimation of Random Coefficients Discrete Choice Models," Research Reports 149208, University of Connecticut, Food Marketing Policy Center.
    • Handle: RePEc:ags:uconnr:149208
    DOI: 10.22004/ag.econ.149208
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