Inference in Linear Regression Models with Many Covariates and Heteroscedasticity

• 成果类型: Article
• 署名作者: Cattaneo, Matias D.; Jansson, Michael; Newey, Whitney K.
• 署名单位: University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of California System; University of California Berkeley; Aarhus University; CREATES; Massachusetts Institute of Technology (MIT)
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2017.1328360
• 发表日期: 2018
• 页码: 1350-1361
• 关键词: Asymptotic Normality; misspecified models; convergence-rates; robust regression; Wild Bootstrap; subclassification; jackknife

摘要

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.