Brazilian Journal of Probability and Statistics

New heteroskedasticity-robust standard errors for the linear regression model

Francisco Cribari-Neto and Maria da Glória A. Lima

Full-text: Open access

Abstract

Linear regressions fitted to cross-sectional data oftentimes display heteroskedasticity, that is, error variances that are not constant. A common modeling strategy consists of estimating the regression parameters by ordinary least squares and then performing hypothesis testing inference using standard errors that are robust to heteroskedasticity. These tests have the correct size asymptotically regardless of whether the error variances are constant. In finite samples, however, they can be quite size-distorted. In this paper, we propose new heteroskedasticity-consistent covariance matrix estimators that deliver more reliable testing inferences in samples of small sizes.

Article information

Source
Braz. J. Probab. Stat., Volume 28, Number 1 (2014), 83-95.

Dates
First available in Project Euclid: 5 February 2014

Permanent link to this document
https://projecteuclid.org/euclid.bjps/1391611339

Digital Object Identifier
doi:10.1214/12-BJPS196

Mathematical Reviews number (MathSciNet)
MR3165430

Zentralblatt MATH identifier
06291462

Keywords
Covariance matrix estimation heteroskedasticity linear regression quasi-$t$ test

Citation

Cribari-Neto, Francisco; Lima, Maria da Glória A. New heteroskedasticity-robust standard errors for the linear regression model. Braz. J. Probab. Stat. 28 (2014), no. 1, 83--95. doi:10.1214/12-BJPS196. https://projecteuclid.org/euclid.bjps/1391611339


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