Work

Nonparametric Estimation in Random Coefficients Binary Choice Models

Year: 2013

Type: article

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 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.

Source: Econometrica

DOI: 10.3982/ecta8675

Authors: Eric Gautier, Yuichi Kitamura

Institutions: Centre de Recherche en Économie et Statistique, ENSAE Paris, Yale University, Cowles Foundation for Research in Economics

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Réguler l'économie au service de la société