Nonparametric Estimation in Random Coefficients Binary Choice Models

• 成果类型: Article
• 署名作者: Gautier, Eric; Kitamura, Yuichi
• 署名单位: Institut Polytechnique de Paris; ENSAE Paris; Yale University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA8675
• 发表日期: 2013
• 页码: 581-607
• 关键词: DENSITY-ESTIMATION; DIRECTIONAL-DATA; deconvolution; distributions; CONVERGENCE

摘要

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 FourierLaplace 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.