A Simple Nonparametric Estimator for the Distribution of Random Coefficients in Discrete Choice Models
Kyoo il Kim () and
Stephen Ryan ()
Additional contact information
Patrick Bajari: University of Minnesota and NBER
No 36, Working Papers from Portuguese Competition Authority
We propose an estimator for discrete choice models, such as the logit, with a nonparametric distribution of random coefficients. The estimator is linear regression subject to linear inequality constraints and is robust, simple to program and quick to compute compared to alternative estimators for mixture models. We discuss three methods for proving identi?fication of the distribution of heterogeneity for any given economic model. We prove the identi?fication of the logit mixtures model, which, surprisingly given the wide use of this model over the last 30 years, is a new result. We also derive our estimator?s non-standard asymptotic distribution and demonstrate its excellent small sample properties in a Monte Carlo. The estimator we propose can be extended to allow for endogenous prices. The estimator can also be used to reduce the computational burden of nested ?fixed point methods for complex models like dynamic programming discrete choice.
Pages: 51 pages
References: Add references at CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed
Downloads: (external link)
http://www.concorrencia.pt/download/WP36_Paper_Bajarietal.pdf First version, 2007 (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 NOT FOUND
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:pca:wpaper:36
Access Statistics for this paper
More papers in Working Papers from Portuguese Competition Authority Contact information at EDIRC.
Bibliographic data for series maintained by Duarte Brito ().