A Simple Nonparametric Estimator for the Distribution of Random Coefficients

Patrick Bajari, Jeremy T. Fox, Kyoo il Kim, Stephen P. Ryan

NBER Working Paper No. 15210
Issued in August 2009
NBER Program(s):Economics of Education, Industrial Organization, Labor Studies, Productivity, Innovation, and Entrepreneurship, Technical Working Papers

We propose a simple nonparametric mixtures estimator for recovering the joint distribution of parameter heterogeneity in economic models, such as the random coefficients logit. The estimator is based on linear regression subject to linear inequality constraints, and is robust, easy to program and computationally attractive compared to alternative estimators for random coefficient models. We prove consistency and provide the rate of convergence under deterministic and stochastic choices for the sieve approximating space. We present a Monte Carlo study and an empirical application to dynamic programming discrete choice with a serially-correlated unobserved state variable.

Acknowledgments

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Document Object Identifier (DOI): 10.3386/w15210

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