The Bias of a Heteroskedasticity Consistent Covariance Matrix Estimator

Chesher, Andrew; Jewitt, Ian

doi:10.2307/1911269

Cited by 161 publications

(122 citation statements)

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“…To account for heteroskedasticity, inference is based on the bias-adjusted HC3 variancecovariance matrix estimator, favored by Chesher and Jewitt (1987) and Cribari-Neto, Ferrari, and Oliveira (2005). To account for bias due to endogeneity of certain righthand-side control variables, we estimate the model using instrumental variables.…”

Section: Model and Methodsmentioning

confidence: 99%

Special-Interest Groups and Volatility

2007

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Section: Model and Methodsmentioning

confidence: 99%

Special-Interest Groups and Volatility

2007

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“…There are valuable analytical results in Chesher and Jewitt (1987), Chesher (1989), and Chesher and Austin (1991). When the sample is balanced, with no points of high leverage, these papers find that HC1, HC2, and HCJ all tend to work quite well.…”

Section: Better Hccmesmentioning

confidence: 99%

Thirty Years of Heteroskedasticity-Robust Inference

MacKinnon

2012

Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. White (1980) marked the beginning of a new era for inference in econometrics. It introduced the revolutionary idea of inference that is robust to heteroskedasticity of unknown form, an idea that was very soon extended to other forms of robust inference and also led to many new estimation methods. This paper discusses the development of heteroskedasticity-robust inference since 1980. There have been two principal lines of investigation. One approach has been to modify White's original estimator to improve its finite-sample properties, and the other has been to use bootstrap methods. The relation between these two approaches, and some ways in which they may be combined, are discussed. Finally, a simulation experiment compares various methods and shows how far heteroskedasticity-robust inference has come in just over thirty years. Terms of use: Documents in EconStor may

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“…Important papers whose results are related to ours include White (1980), MacKinnon and White (1985), Wu (1986), Chesher and Jewitt (1987), Shao and Wu (1987), Chesher (1989), Cribari-Neto, Ferrari, and Cordeiro (2000), Bera, Suprayitno, and Premaratne (2002), Stock and Watson (2008),…”

Section: Introductionmentioning

confidence: 76%

Treatment effects with many covariates and heteroskedasticity

Cattaneo

Jansson²,

Newey³

2015

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe linear regression model is widely used in empirical work in Economics. Researchers often include many covariates in their linear model speci…cation in an attempt to control for confounders. We give inference methods that allow for many covariates and heteroskedasticity. Our results are obtained using high-dimensional approximations, where the number of covariates are allowed to grow as fast as the sample size. We …nd that all of the usual versions of EickerWhite heteroskedasticity consistent standard error estimators for linear models are inconsistent under this asymptotics. We then propose a new heteroskedasticity consistent standard error formula that is fully automatic and robust to both (conditional) heteroskedasticity of unknown form and the inclusion of possibly many covariates. We apply our …ndings to three settings: (i) parametric linear models with many covariates, (ii) semiparametric semi-linear models with many technical regressors, and (iii) linear panel models with many …xed e¤ects.

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The Bias of a Heteroskedasticity Consistent Covariance Matrix Estimator

Cited by 161 publications

References 7 publications

Special-Interest Groups and Volatility

Special-Interest Groups and Volatility

Thirty Years of Heteroskedasticity-Robust Inference

Treatment effects with many covariates and heteroskedasticity

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