Heteroscedasticity-Robust Inference in Linear Regression Models With Many Covariates

• 成果类型: Article
• 署名作者: Jochmans, Koen
• 署名单位: University of Cambridge
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1831924
• 发表日期: 2022
• 页码: 887-896
• 关键词: Asymptotic Normality; variance-estimation; convergence-rates; matrix

摘要

We consider inference in linear regression models that is robust to heteroscedasticity and the presence of many control variables. When the number of control variables increases at the same rate as the sample size the usual heteroscedasticity-robust estimators of the covariance matrix are inconsistent. Hence, tests based on these estimators are size distorted even in large samples. An alternative covariance-matrix estimator for such a setting is presented that complements recent work by Cattaneo, Jansson, and Newey. We provide high-level conditions for our approach to deliver (asymptotically) size-correct inference as well as more primitive conditions for three special cases. Simulation results and an empirical illustration to inference on the union premium are also provided. for this article are available online.