Heteroscedasticity-Robust Inference in Linear Regression Models With Many Covariates

Koen Jochmans

Journal of the American Statistical Association, 2022, vol. 117, issue 538, 887-896

Abstract: 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. Supplementary materials for this article are available online.

Date: 2022
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Working Paper: Heteroskedasticity-Robust Inference in Linear Regression Models with Many Covariates (2020)
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DOI: 10.1080/01621459.2020.1831924

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