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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Edited by:
Tong Li
Language:
English
First published:
January 1, 2011
Publication Frequency:
2 issues per year
About this journal
Objective
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.
Topics
- Estimation and testing
- Cross-sectional, panel-data, and time-series methodologies
- Financial and macro econometric methods
- Quantile regression
- Nonparametric estimation
- Bootstrap inference
- Teaching econometrics
Article formats
Research article submissions should make a significant contribution to the existing econometrics literature. This contribution can consist of new methodology, new theoretical results, new computational methods, comparison of existing methodologies, etc. Upon acceptance of their paper, authors are required to provide any data and computer programs so that readers can replicate results and utilize the econometric methods for their own research. Papers will not be published until these materials have been made available.
In addition to traditional research articles, the journal will also publish contributions to its “Practitioners’ corner” and “Teaching corner” sections:
Practitioners’ corner: This section publish “how-to” and survey papers for practitioners. Papers in the section can include: detailed treatments of specific econometric methods not covered extensively (or well) elsewhere, surveys that bring empirical researchers up-to-date on quickly developing research areas, etc.
Teaching corner: This section publish papers that are directly useful for econometrics instructors at the undergraduate or graduate level. Papers in the section can provide datasets (with completely worked-through analysis) that help instructors to illustrate specific models and/or methodologies. Papers in this section can also provide other tools for instructors, including novel suggestions for presenting certain material, simulation designs, etc.
> Information on submission processYour Benefits
Your benefits:
- Theoretical and applied approach to econometrics
- Direct relevance to empirical economics research
- Imperative topics of broad interest to practitioners
- Up-to-date empirical tools for practitioners and instructors
- Cutting-edge methods applicable to a wide range of empirical problems
- Detailed methodologies that can be easily implemented
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Requires Authentication UnlicensedA Moment-Based Representation for Heteroskedasticity Robust Standard ErrorsLicensedOctober 6, 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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You can easily submit your manuscript online. Simply go to...
http://mc.manuscriptcentral.com/dgjem
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- Rapid online ahead-of-print publication with short turnaround times
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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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Supplementary Materials
Topics
Journal information
Additional information
eISSN:
2156-6674
Language:
English
Publisher:
De Gruyter
Additional information
First published:
January 1, 2011
Publication Frequency:
2 issues per year
eISSN:
2156-6674
Audience(s) for this journal
researchers, practitioners and instructors in the field of econometrics
