TY  - JOUR
AU  - Bajari,Patrick
AU  - Fox,Jeremy T.
AU  - Kim,Kyoo il
AU  - Ryan,Stephen P.
TI  - A Simple Nonparametric Estimator for the Distribution of Random Coefficients
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 15210
PY  - 2009
Y2  - August 2009
UR  - http://www.nber.org/papers/w15210
L1  - http://www.nber.org/papers/w15210.pdf
N1  - Author contact info:
Patrick Bajari
University of Washington
331 Savery Hall
UW Economics Box 353330
Seattle, Washington 98195-3330
E-Mail: Bajari@uw.edu

Jeremy T. Fox
Economics Department
University of Michigan
238 Lorch Hall
611 Tappan Ave
Ann Arbor, MI 48109
Tel: 734-330-2854
Fax: 734-274-2331
E-Mail: jeremyfox@gmail.com

Kyoo il Kim
Department of Economics
University of Minnesota
4-129 Hanson Hall
1925 4th Street South
Minneapolis, MN 55455
Tel: 612-625-6793
E-Mail: kyookim@umn.edu

Stephen P. Ryan
Department of Economics
University of Texas at Austin
BRB 3.134D
2225 Speedway Stop C3100
Austin, TX 78712-1690
Tel: 512/425-8543
E-Mail: sryan@utexas.edu
AB  - 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.
ER  -