nep-dcm 2009-08-30 papers

on Discrete Choice Models

Issue of 2009‒08‒30
one paper chosen by
Philip Yu
Hong Kong University

Nonparametric Estimation in Random Coefficients Binary Choice Models By Eric Gautier; Yuichi Kitamura

Nonparametric Estimation in Random Coefficients Binary Choice Models

By:	Eric Gautier (ENSAE-CREST); Yuichi Kitamura (Cowles Foundation, Yale University)
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 non-random coefficients and models with endogeneity are discussed.
Keywords:	Inverse problems, Discrete choice models
JEL:	C14 C25
Date:	2009–08
URL:	http://d.repec.org/n?u=RePEc:cwl:cwldpp:1721&r=dcm

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