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• W2113256656 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." @default.
• W2113256656 created "2016-06-24" @default.
• W2113256656 date "2013-01-01" @default.
• W2113256656 modified "2023-09-24" @default.
• W2113256656 title "Nonparametric Estimation in Random Coefficients Binary Choice Models" @default.
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• W2113256656 doi "https://doi.org/10.3982/ecta8675" @default.
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