Econometrica: Mar 2013, Volume 81, Issue 2

Nonparametric Estimation in Random Coefficients Binary Choice Models

https://doi.org/10.3982/ECTA8675
p. 581-607

Eric Gautier, Yuichi Kitamura

This paper considers random coefficients binary choice models. The main goal is to estimate the density of the random coefficients nonparametrically. This is an ill‐posed inverse problem characterized by an integral transform. A new density estimator for the random coefficients is developed, utilizing Fourier–Laplace series on spheres. This approach offers a clear insight on the identification problem. More importantly, it leads to a closed form estimator formula that yields a simple plug‐in procedure requiring no numerical optimization. The new estimator, therefore, is easy to implement in empirical applications, while being flexible about the treatment of unobserved heterogeneity. Extensions including treatments of nonrandom coefficients and models with endogeneity are discussed.

Log In To View Full Content

Supplemental Material

Supplement to "Nonparametric Estimation in Random Coefficients Binary Choice Models"

This zip file contains the replication files for the manuscript.

Read More View ZIP


Supplement to "Nonparametric Estimation in Random Coefficients Binary Choice Models"

This appendix contains some materials that were omitted in the manuscript as well as technical proofs.

Read More View PDF


Back