Semiparametric Bayesian Estimation of Random Coefficients Discrete Choice Models
| dc.creator | Tchumtchoua, Sylvie | |
| dc.creator | Dey, Dipak | |
| dc.date | 2017-04-01T20:09:38Z | |
| dc.date.accessioned | 2026-07-09T07:06:49Z | |
| dc.description | 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. | |
| dc.identifier | doi:10.22004/ag.econ.149208 | |
| dc.identifier | https://ageconsearch.umn.edu/record/149208/files/rr102.pdf | |
| dc.identifier | http://ageconsearch.umn.edu/record/149208 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/584618 | |
| dc.language | eng | |
| dc.publisher | ||
| dc.source | http://ageconsearch.umn.edu/record/149208 | |
| dc.title | Semiparametric Bayesian Estimation of Random Coefficients Discrete Choice Models | |
| dc.type | Text |