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摘要翻译：
本文给出了线性回归模型的截距的一个新估计，当结果服从一个选择规则时，它是可变的。拦截往往是在这种背景下的固有利益；例如，在方案评估上下文中，参与者和非参与者的结果方程中截取的差异可以解释为参与者的平均结果和如果他们选择不参与的话他们的反事实平均结果的差异。在温和的条件下，新估计的收敛速度等于$n^{-p/(2p+1)}，其中$p\ge2$是一个整数，它反映了某些光滑性假设的强度。这种收敛速度在本文中被表示为根据极小极大准则估计截距参数的最优收敛速度。与文献中的其他建议不同，新的估计量在温和的条件下是一致的和渐近正态的，收敛速度是相同的，无论选择在多大程度上依赖于结果方程中的不可观察项。包括仿真证据和一个经验实例。
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英文标题：
《Rate-Optimal Estimation of the Intercept in a Semiparametric
Sample-Selection Model》
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作者：
Chuan Goh
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最新提交年份：
2018
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
This paper presents a new estimator of the intercept of a linear regression model in cases where the outcome varaible is observed subject to a selection rule. The intercept is often in this context of inherent interest; for example, in a program evaluation context, the difference between the intercepts in outcome equations for participants and non-participants can be interpreted as the difference in average outcomes of participants and their counterfactual average outcomes if they had chosen not to participate. The new estimator can under mild conditions exhibit a rate of convergence in probability equal to $n^{-p/(2p+1)}$, where $p\ge 2$ is an integer that indexes the strength of certain smoothness assumptions. This rate of convergence is shown in this context to be the optimal rate of convergence for estimation of the intercept parameter in terms of a minimax criterion. The new estimator, unlike other proposals in the literature, is under mild conditions consistent and asymptotically normal with a rate of convergence that is the same regardless of the degree to which selection depends on unobservables in the outcome equation. Simulation evidence and an empirical example are included.
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PDF链接：
https://arxiv.org/pdf/1710.01423
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