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摘要翻译：
本文讨论了当存在多个控制变量时，可能大于样本量的差中差(DID)估计。在这种情况下，需要有限数量变量的传统估计方法就不起作用了。人们可以考虑使用统计或机器学习(ML)方法。然而，由Chernozhukov等人提出的著名的ML推理理论方法。(2018)，直接将ML方法应用于传统的半参数DID估计会造成显著的偏差，使得这些DID估计不能满足sqrt{N}一致。本文针对三种不同的数据结构，提出了三种新的DID估计器，在应用ML方法时，它们能够减小偏差，并达到sqrt{N}-相合性和均值为零的渐近正态性。这导致了直接的推论过程。另外，我证明了这些新估计具有小偏差性质(SBP)，这意味着它们的偏差将比它所基于的非参数估计的点态偏差更快地收敛到零。
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英文标题：
《Semiparametric Difference-in-Differences with Potentially Many Control
Variables》
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作者：
Neng-Chieh Chang
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最新提交年份：
2019
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分类信息：
一级分类：Economics 经济学
二级分类：General Economics 一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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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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一级分类：Quantitative Finance 数量金融学
二级分类：Economics 经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
This paper discusses difference-in-differences (DID) estimation when there exist many control variables, potentially more than the sample size. In this case, traditional estimation methods, which require a limited number of variables, do not work. One may consider using statistical or machine learning (ML) methods. However, by the well-known theory of inference of ML methods proposed in Chernozhukov et al. (2018), directly applying ML methods to the conventional semiparametric DID estimators will cause significant bias and make these DID estimators fail to be sqrt{N}-consistent. This article proposes three new DID estimators for three different data structures, which are able to shrink the bias and achieve sqrt{N}-consistency and asymptotic normality with mean zero when applying ML methods. This leads to straightforward inferential procedures. In addition, I show that these new estimators have the small bias property (SBP), meaning that their bias will converge to zero faster than the pointwise bias of the nonparametric estimator on which it is based.
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PDF链接：
https://arxiv.org/pdf/1812.10846
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