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.
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| Length: | 49 pages |
| Date of creation: | Oct 2007 |
| Handle: | RePEc:zwi:fpcrep:102 |
| Contact details of provider: | Postal: 1376 Storrs Road, U-21, Storrs, Connecticut 06269-4021 Phone: 860-486-2836 Fax: 860-486-1932 Web page: http://www.zwickcenter.uconn.edu Email: More information through EDIRC
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