Nonparametric estimation in random coefficients binary choice models

Eric Gautier and Yuichi Kitamura ()
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Yuichi Kitamura: Cowles Foundation for Research in Economics - Yale University [New Haven]

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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)
Date: 2011-09-01
Note: View the original document on HAL open archive server: https://hal.science/hal-00403939v2
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Citations: View citations in EconPapers (17)

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Related works:
Journal Article: Nonparametric Estimation in Random Coefficients Binary Choice Models (2013)
Working Paper: Nonparametric Estimation in Random Coefficients Binary Choice Models (2009)
Working Paper: Nonparametric Estimation in Random Coefficients Binary Choice Models (2008)
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