Semiparametric Bayesian Estimation of Random Coefficients Discrete Choice Models
AbstractHeterogeneity 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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Bibliographic InfoPaper provided by University of Connecticut, Department of Agricultural and Resource Economics, Charles J. Zwick Center for Food and Resource Policy in its series Food Marketing Policy Center Research Reports with number 102.
Length: 49 pages
Date of creation: Oct 2007
Date of revision:
Dependent Dirichlet process; Discrete choice models; Heterogeneity; Markov chain; Monte Carlo;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
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