nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2009‒08‒30
one paper chosen by
Philip Yu
Hong Kong University

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

  1. 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

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