标题： 局部稳健的无排除限制样本选择模型的半参数估计 显示英文标题

标题： Locally robust semiparametric estimation of sample selection models without exclusion restrictions

摘要： 现有的半参数样本选择模型的识别和估计方法严重依赖于排除限制。 然而，在实践中很难找到一个可信的排除变量，该变量与选择有关但与结果无关。 在本文中，我们建立了没有排除限制的半参数样本选择模型的新识别结果。 关键的识别假设是选择方程的非线性和结果方程的线性。 函数形式的差异起到了排除变量的作用，并提供了识别能力。 根据识别结果，我们提出通过带有非参数生成解释变量的部分线性回归来估计模型。 为了在生成解释变量时适应现代机器学习方法，我们通过添加第一步的影响函数构造了一个正交化的矩条件，并通过求解交叉拟合的正交化矩条件开发了一个局部稳健估计量。 我们在较弱的正则性条件下证明了所提出的估计量的根n一致性和渐近正态性。 蒙特卡洛模拟显示了估计量在有限样本中的良好性能，对工资回归的应用说明了其在没有排除限制情况下的实用性。 显示英文摘要

摘要： Existing identification and estimation methods for semiparametric sample selection models rely heavily on exclusion restrictions. However, it is difficult in practice to find a credible excluded variable that has a correlation with selection but no correlation with the outcome. In this paper, we establish a new identification result for a semiparametric sample selection model without the exclusion restriction. The key identifying assumptions are nonlinearity on the selection equation and linearity on the outcome equation. The difference in the functional form plays the role of an excluded variable and provides identification power. According to the identification result, we propose to estimate the model by a partially linear regression with a nonparametrically generated regressor. To accommodate modern machine learning methods in generating the regressor, we construct an orthogonalized moment by adding the first-step influence function and develop a locally robust estimator by solving the cross-fitted orthogonalized moment condition. We prove root-n-consistency and asymptotic normality of the proposed estimator under mild regularity conditions. A Monte Carlo simulation shows the satisfactory performance of the estimator in finite samples, and an application to wage regression illustrates its usefulness in the absence of exclusion restrictions.