Nonparametric estimation in random coefficients binary choice models

Abstract

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 prob- lem 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 hetero- geneity. Extensions including treatments of non-random coefficients and models with endogeneity are discussed.

Reference

Eric Gautier, and Yuichi Kitamura, “Nonparametric estimation in random coefficients binary choice models”, Econometrica, vol. 81, 2013, pp. 581–607.