Inference in Linear Regression Models with Many Covariates and Heteroscedasticity

Matias Cattaneo, Michael Jansson and Whitney Newey

Journal of the American Statistical Association, 2018, vol. 113, issue 523, 1350-1361

Abstract: The linear regression model is widely used in empirical work in economics, statistics, and many other disciplines. Researchers often include many covariates in their linear model specification in an attempt to control for confounders. We give inference methods that allow for many covariates and heteroscedasticity. Our results are obtained using high-dimensional approximations, where the number of included covariates is allowed to grow as fast as the sample size. We find that all of the usual versions of Eicker–White heteroscedasticity consistent standard error estimators for linear models are inconsistent under this asymptotics. We then propose a new heteroscedasticity consistent standard error formula that is fully automatic and robust to both (conditional) heteroscedasticity of unknown form and the inclusion of possibly many covariates. We apply our findings to three settings: parametric linear models with many covariates, linear panel models with many fixed effects, and semiparametric semi-linear models with many technical regressors. Simulation evidence consistent with our theoretical results is provided, and the proposed methods are also illustrated with an empirical application. Supplementary materials for this article are available online.

Date: 2018
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Citations: View citations in EconPapers (44)

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Related works:
Working Paper: Inference in Linear Regression Models with Many Covariates and Heteroscedasticity (2018)
Working Paper: Inference in Linear Regression Models with Many Covariates and Heteroskedasticity (2017)
Working Paper: Inference in linear regression models with many covariates and heteroskedasticity (2017)
Working Paper: Inference in linear regression models with many covariates and heteroskedasticity (2017)
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DOI: 10.1080/01621459.2017.1328360

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