A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity

Why this mattered

TBD

Abstract

This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal model of the structure of the heteroskedasticity. By comparing the elements of the new estimator to those of the usual covariance estimator, one obtains a direct test for heteroskedasticity, since in the absence of heteroskedasticity, the two estimators will be approximately equal, but will generally diverge otherwise. The test has an appealing least squares interpretation

Related

• cite → Specification Tests in Econometrics — White’s heteroskedasticity-consistent covariance estimator builds on Hausman-style specification testing by targeting misspecified error variance in econometric models.
• cite ← Large Sample Properties of Generalized Method of Moments Estimators — Hansen's GMM asymptotic covariance theory incorporates White's heteroskedasticity-consistent covariance estimation.

Sources

• DOI: https://doi.org/10.2307/1912934
• OpenAlex: https://openalex.org/W2108818539