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A Simple Nonparametric Estimator for the Distribution of Random Coefficients in Discrete Choice Models (2008)
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BibTeX
@MISC{Bajari08asimple,
author = {Patrick Bajari and Jeremy T. Fox and Kyoo Il Kim and Stephen Ryan},
title = {A Simple Nonparametric Estimator for the Distribution of Random Coefficients in Discrete Choice Models},
year = {2008}
}
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Abstract
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 identification of the distribution of heterogeneity for any given economic model. We prove the identification 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.
Keyphrases
discrete choice model random coefficient simple nonparametric estimator logit mixture model monte carlo mixture model nested fixed point method alternative estimator inequality constraint computational burden excellent small sample property complex model endogenous price discrete choice non-standard asymptotic distribution new result wide use nonparametric distribution economic model linear regression subject
