Semiparametric Bayesian Estimation of Random Coefficients Discrete Choice Models

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.


Issue Date:
2007-10
Publication Type:
Report
PURL Identifier:
http://purl.umn.edu/149208
Total Pages:
49
JEL Codes:
C11; C14; C25
Series Statement:
Research Report
102




 Record created 2017-04-01, last modified 2017-12-11

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