Sample Selection Models with Monotone Control Functions
Sample Selection Models with Monotone Control Functions
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DOI：
10.2139/ssrn.3420924
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Ruixuan Liu;Zhengfei Yu
中科院分区：
文献类型：
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作者：
Ruixuan Liu;Zhengfei Yu
著名的Heckman选择模型产生一个与米尔斯比的倒数成正比的选择校正函数（控制函数），它是单调的。本文研究了一个样本选择模型，该模型不对潜在误差项施加参数分布假设，同时保持控制函数的单调性。我们表明，正（负）依赖条件的潜在误差项是足够的控制函数的单调性。该条件等价于对潜在误差项的copula函数的限制。利用单调性，我们提出了一种无调整参数的半参数估计方法，并证明了有限维参数估计的根n -相合性和渐近正态性。一个新的测试的选择性也发展的形状限制的存在下。仿真和实证应用进行说明所提出的方法的实用性。
Abstract The celebrated Heckman selection model yields a selection correction function (control function) proportional to the inverse Mills ratio, which is monotone. This paper studies a sample selection model that does not impose parametric distributional assumptions on the latent error terms, while maintaining the monotonicity of the control function. We show that a positive (negative) dependence condition on the latent error terms is sufficient for the monotonicity of the control function. The condition is equivalent to a restriction on the copula function of latent error terms. Using the monotonicity, we propose a tuning-parameter-free semiparametric estimation method and establish root n -consistency and asymptotic normality for the estimates of finite-dimensional parameters. A new test for selectivity is also developed in the presence of the shape restriction. Simulations and an empirical application are conducted to illustrate the usefulness of the proposed methods.