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.
|Date of creation:||Oct 2007|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.zwickcenter.uconn.edu
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:zwi:fpcrep:102. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If references are entirely missing, you can add them using this form.