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sidenav header background[3月3日] 计量金融与大数据分析workshop
发布日期：2023-02-27 01:00 来源：
Cluster Robust Inference in Linear Regression Models with Many CovariatesTime: 10:00-11:30, Mar-3 2023Location: 107, SOE, PKUPresenter: Aibo GongAbstract: Researchers often include many covariates in their linear regression models to control for confounders in empirical research in economics, statistics and social sciences. It is also common practice in empirical work to use cluster-robust standard errors. In this project, we develop inference methods that are robust to the presence of many covariates and to clustering. We find that when the number of included covariates grows at the same rate as the sample size, the commonly used Liang-Zeger and HC-k cluster robust standard errors are invalid in general. We propose cluster robust standard error formulas that are robust to the inclusion of possibly many covariates from different approaches and apply the standard errors under different setups. Simulation evidence supporting our theoretical results is also provided.Bio：巩爱博教授现为北京大学经济学院助理教授，密西根大学博士，主要研究领域为计量经济学，相关研究已经发表在Journal of Economic Theory国际顶级期刊。
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sidenav background教学项目
sidenav header background[3月3日] 计量金融与大数据分析workshop
发布日期：2023-02-27 01:00 来源：
Cluster Robust Inference in Linear Regression Models with Many CovariatesTime: 10:00-11:30, Mar-3 2023Location: 107, SOE, PKUPresenter: Aibo GongAbstract: Researchers often include many covariates in their linear regression models to control for confounders in empirical research in economics, statistics and social sciences. It is also common practice in empirical work to use cluster-robust standard errors. In this project, we develop inference methods that are robust to the presence of many covariates and to clustering. We find that when the number of included covariates grows at the same rate as the sample size, the commonly used Liang-Zeger and HC-k cluster robust standard errors are invalid in general. We propose cluster robust standard error formulas that are robust to the inclusion of possibly many covariates from different approaches and apply the standard errors under different setups. Simulation evidence supporting our theoretical results is also provided.Bio：巩爱博教授现为北京大学经济学院助理教授，密西根大学博士，主要研究领域为计量经济学，相关研究已经发表在Journal of Economic Theory国际顶级期刊。
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