Endogenous selection or treatment model estimation
Endogenous selection or treatment model estimation
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DOI：
10.1016/j.jeconom.2006.11.004
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发表时间：
2007-12-01
影响因子：
6.3
通讯作者：
Lewbel, Arthur
中科院分区：
文献类型：
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作者：
Lewbel, Arthur
在样本选择或处理效应模型中，常见的不可观测变量可能会以未知的方式影响结果和选择的概率。本文证明了在给定一个观察变量V的情况下，潜在结果的分布函数是以协变量为条件的，该变量以某种方式影响治疗或选择概率，并且有条件地独立于潜在结果模型中的误差项。提供了基于该辨识的选择模型估计器，其形式为简单加权平均、GMM或两阶段最小二乘。这些估计量允许内生的和错误测量的回归变量。给出了企业投资模型和教育对工资影响模型的估计的实证应用。(C)2006爱思唯尔B.V.保留所有权利。
In a sample selection or treatment effects model, common unobservables may affect both the outcome and the probability of selection in unknown ways. This paper shows that the distribution function of potential outcomes, conditional on covariates, can be identified given an observed variable V that affects the treatment or selection probability in certain ways and is conditionally independent of the error terms in a model of potential outcomes. Selection model estimators based on this identification are provided, which take the form of simple weighted averages, GMM, or two stage least squares. These estimators permit endogenous and mismeasured regressors. Empirical applications are provided to estimation of a firm investment model and a schooling effects on wages model. (c) 2006 Elsevier B.V. All rights reserved.