Estimation of A Class of Nonparameteric Probit Models

Sungwon Lee
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Abstract

In this paper, we consider a class of nonparametric probit models for binary dependent variables. We extend the standard parametric probit models to nonparametric models in which the parametric index function in the standard probit models is nonparametrically specified. Such a nonparametric specification for the index function in probit models allows us to consider a flexible functional form of the structural parameters. We propose to use a sieve maximum likelihood estimation approach to estimate the parameter and develop the asymptotic theory, including consistency, convergence rates, and asymptotic normality. Our asymptotic normality results are applicable to a wide class of functionals of the parameter, regardless of whether the functional of interest is regular or irregular. The Monte Carlo simulation result shows that the proposed sieve maximum likelihood estimator performs well in finite samples in the sense that the proposed sieve maximum likelihood estimator has a small variance and negligible bias in finite samples.
一类非参数概率模型的估计
本文考虑了一类二元因变量的非参数概率模型。我们将标准参数probit模型推广到非参数模型,其中标准probit模型中的参数索引函数是非参数指定的。probit模型中索引函数的这种非参数规范允许我们考虑结构参数的灵活函数形式。我们建议使用筛子极大似然估计方法来估计参数,并发展渐近理论,包括一致性,收敛率和渐近正态性。我们的渐近正态性结果适用于参数泛函的广泛类别,而不管我们感兴趣的泛函是正则的还是不规则的。蒙特卡罗仿真结果表明,所提出的筛极大似然估计量在有限样本中方差小,偏差可忽略不计,在有限样本中表现良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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