Nonparametric Estimation in Random Coefficients Binary Choice Models
Nonparametric Estimation in Random Coefficients Binary Choice Models
复制标题
随机系数二元选择模型中的非参数估计
DOI：
10.2139/ssrn.1459091
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Y. Kitamura
中科院分区：
文献类型：
--
作者：
E. Gautier;Y. Kitamura
本文考虑随机系数二元选择模型。主要目标是非参数地估计随机系数的密度。这是一个不适定的反问题，其特征在于一个积分变换。利用球面上的Fourier-Laplace级数，给出了随机系数的一种新的密度估计。这种方法提供了一个明确的洞察力的识别问题。更重要的是，它导致了一个封闭的形式估计公式，产生一个简单的插件程序，不需要数值优化。因此，新的估计量，很容易实现的经验应用，同时对未观察到的异质性的治疗是灵活的。扩展，包括处理非随机系数和模型的endoadministration进行了讨论。
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.