Nonparametric Estimation in Random Coefficients Binary Choice Models

Eric Gautier and Yuichi Kitamura ()
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Eric Gautier: ENSAE-CREST
Yuichi Kitamura: Cowles Foundation, Yale University, http://cowles.econ.yale.edu/faculty/kitamura.htm

No 1721, Cowles Foundation Discussion Papers from 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 (search for similar items in EconPapers)
JEL-codes: C14 C25 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dcm and nep-ecm
Date: 2009-08
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