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摘要翻译：
在使用线性回归模型估计某些结构/因果效应时，使用聚类稳健标准误差是经验工作中的常见做法。研究人员还经常在他们的模型规范中包括一大组回归子，以便控制观察到的和未观察到的混杂。在本文中，我们发展了多控制和聚类线性回归模型的推理方法。我们证明了当控制数为样本容量的非消失分数时，Liang和Zeger(1986)基于通常的聚类鲁棒标准差的推断一般是无效的。然后，我们提出了一个新的聚类标准误差公式，该公式对包含许多控制项具有鲁棒性，并允许在各种高维线性回归模型中进行有效的推断，包括固定效应面板数据模型和半参数部分线性模型。蒙特卡罗证据支持了我们的理论结果，并表明我们提出的方差估计在有限样本中表现良好。提出的方法还通过重新访问Donohue III和Levitt(2001)关于堕胎对犯罪影响的研究的实证应用加以说明。
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英文标题：
《Cluster-Robust Standard Errors for Linear Regression Models with Many
Controls》
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作者：
Riccardo D'Adamo
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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：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：
It is common practice in empirical work to employ cluster-robust standard errors when using the linear regression model to estimate some structural/causal effect of interest. Researchers also often include a large set of regressors in their model specification in order to control for observed and unobserved confounders. In this paper we develop inference methods for linear regression models with many controls and clustering. We show that inference based on the usual cluster-robust standard errors by Liang and Zeger (1986) is invalid in general when the number of controls is a non-vanishing fraction of the sample size. We then propose a new clustered standard errors formula that is robust to the inclusion of many controls and allows to carry out valid inference in a variety of high-dimensional linear regression models, including fixed effects panel data models and the semiparametric partially linear model. Monte Carlo evidence supports our theoretical results and shows that our proposed variance estimator performs well in finite samples. The proposed method is also illustrated with an empirical application that re-visits Donohue III and Levitt's (2001) study of the impact of abortion on crime.
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PDF链接：
https://arxiv.org/pdf/1806.07314
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