随机系数二元响应模型的非参数极大似然方法
Nonparametric maximum likelihood methods for binary response models with random coefficients
摘要：
具有随机系数的二元响应的单指标线性模型已广泛应用于许多计量经济环境中，其随机系数的分布具有不同的参数规格。相比之下，由Cosslett（1983）和Ichimura and Thompson（1998）提出的非参数极大似然估计（NPMLE）在应用工作中受到的关注较少，主要是由于计算困难。我们提出了一种新的方法来计算NPMLEs的二进制响应模型，显着增加其计算的易处理性，从而促进更大的灵活性，在应用中。我们的方法依赖于涉及超平面排列几何的最新发展，与Gautier和Kitamura（2013）最近提出的去卷积方法形成对比。在华盛顿特区工作的旅程模式选择的应用程序说明了方法。
英文摘要：
Single index linear models for binary response with random coefficients have been extensively employed in many econometric settings under various parametric specifications of the distribution of the random coefficients. Nonparametric maximum likelihood estimation (NPMLE) as proposed by Cosslett (1983) and Ichimura and Thompson (1998), in contrast, has received less attention in applied work due primarily to computational difficulties. We propose a new approach to computation of NPMLEs for binary response models that significantly increase their computational tractability thereby facilitating greater flexibility in applications. Our approach, which relies on recent developments involving the geometry of hyperplane arrangements, is contrasted with the recently proposed deconvolution method of Gautier and Kitamura (2013). An application to modal choice for the journey to work in the Washington DC area illustrates the methods.
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