Semiparametric estimation of binary response models with endogenous regressors
Semiparametric estimation of binary response models with endogenous regressors
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DOI：
10.1016/j.jeconom.2009.04.005
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发表时间：
2009-11
影响因子：
6.3
通讯作者：
C. Rothe
中科院分区：
文献类型：
--
作者：
C. Rothe
在本文中，我们提出了一个两步半参数极大似然（SML）估计的系数时，通过控制函数的方法实现识别的单指标二元选择模型的内生回归。第一步包括估计内源回归量的简化形式方程和提取相应的残差。在第二步中，后者作为控制变量添加到结果方程中，结果方程又由SML估计。我们建立了估计量的n-相合性和渐近正态性。在模拟研究中，我们比较了我们的估计与现有的替代品的属性，突出了我们的方法的优势。
In this paper, we propose a two-step semiparametric maximum likelihood (SML) estimator for the coefficients of a single index binary choice model with endogenous regressors when identification is achieved via a control function approach. The first step consists of estimating a reduced form equation for the endogenous regressors and extracting the corresponding residuals. In the second step, the latter are added as control variates to the outcome equation, which is in turn estimated by SML. We establish the estimator’s n-consistency and asymptotic normality. In a simulation study, we compare the properties of our estimator with those of existing alternatives, highlighting the advantages of our approach.