Heteroskedasticity robust standard errors are often presented as a formula that is not directly related to classical standard errors derived under homoskedasticity. This short paper introduces a moment-based result relating these two estimators through the correlation between squared residuals and squared regressors. Though the result does not rely on normality, it admits a simple approximation when all variables are normally distributed. This representation can be useful both for pedagogical purposes in undergraduate courses that do not use matrix algebra and in highlighting the relative magnitude of robust to non-robust standard errors.
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Journal of Econometric Methods
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Herausgegeben von:
Tong Li
Sprache:
Englisch
Erstveröffentlichung:
1. Januar 2011
Erscheinungsweise:
2 Hefte pro Jahr
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The Journal of Econometric Methods welcomes submissions in theoretical and applied econometrics of direct relevance to empirical economics research. The journal aims to bridge the widening gap between econometric research and empirical practice. We aim to publish papers from top scholars in econometrics, but submissions must (i) consider a topic of broad interest to practitioners and (ii) be written in a style that is targeted at practitioners. Subject to these requirements, the journal will consider submissions in all areas of econometrics. We do not consider submissions that are application-specific. While econometric methodology should be thoroughly illustrated with empirical data, such methodology should be useful above and beyond the specific application considered.
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Erfordert eine Authentifizierung Nicht lizenziertA Moment-Based Representation for Heteroskedasticity Robust Standard ErrorsLizenziert6. Oktober 2025
| CiteScore | 2.2 | 2024, Scopus (Elsevier B.V., 2025) |
| SCImago Journal Rank | 0.520 | 2024, SJR (Scimago Lab, 2025; Data Source: Scopus) |
| Source Normalized Impact per Paper | 1.202 | 2024, CWTS Journal Indicators (CWTS B.V., 2025; Data Source: Scopus) |
| Mathematical Citation Quotient | 0.07 |
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Editors
Tong Li, Vanderbilt University, USA
Zhongjun Qu, Boston University, USA
Associate Editors
Dante Amengual, CEMFI, Spain
Stephane Bonhomme, University of Chicago, USA
Christian Brownlees, Pompeu Fabra University, Spain
Xiaohong Chen, Yale University, USA
Xu Cheng, Department of Economics, University of Pennsylvania, USA
Timothy Christensen, University College of London, UK
Sergio Firpo, INSPER, Brazil
Jean-Jacques Forneron, Boston University, Boston, USA
Yu-Chin Hsu, University of Pittsburgh, Pittsburgh, USA
Toru Kitagawa, Brown University, USA
Brendan Kline, University of Texas, Austin, USA
Daniel J. Lewis, University College of London, UK
Laura Liu, Department of Economics, Indiana University, USA
Vadim Marmer, University of British Columbia, Canada
Adam McCloskey, Department of Economics, University of Colorado at Boulder, USA
Tatsushi Oka, Keio University, Japan
Demian Pouzo, University of California, Berkeley, USA
Adam Rosen, Duke University, USA
Yuya Sasaki, Vanderbilt University, USA
Wendun Wang, Erasmus University Rotterdam, the Netherlands
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Weitere Informationen
eISSN:
2156-6674
Sprache:
Englisch
Verlag:
De Gruyter
Weitere Informationen
Erstveröffentlichung:
1. Januar 2011
Erscheinungsweise:
2 Hefte pro Jahr
eISSN:
2156-6674
Zielgruppe(n) für diese Zeitschrift
researchers, practitioners and instructors in the field of econometrics
