Cited by 16,936Advanced Statistical Methods and ModelsFinancial Risk and Volatility ModelingSpatial and Panel Data Analysis

A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix

Newey, Whitney K., West, Kenneth D.·1987·Econometrica

Abstract

This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction. It also establishes consistency of the estimated covariance matrix under fairly general conditions.

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Crossref · 10.2307/1913610

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  • The robustness of the findings is further supported by the Newey and West (1987) t-statistic.

    “The robustness of our findings is further supported by the Newey and West (1987) 𝑡 -statistic, as presented in Table IA 1 of the Internet Appendix.”

    From ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy? (2025) · cites this paper as Newey and West (1987)

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Authors
Newey, W. K., & West, K. D.
Affiliations
National Bureau of Economic Research (US)
Venue
Econometrica
Publisher
JSTOR
Published
1987-05
Volume
55
Issue
3
Pages
703
ISSN
0012-9682 (ISSN-L)
OpenAlex
W2117178635
Cited by
16,936
References
0
Citations by year
2026: 1207 · 2025: 818 · 2024: 783 · 2023: 809 · 2022: 771 · 2021: 800 · 2020: 761 · 2019: 722 · 2018: 708 · 2017: 693 · 2016: 647 · 2015: 682 · 2014: 688 · 2013: 637 · 2012: 677
FWCI
48.33
Type
journal article
Language
en
Open access
closed
Locations
Econometrica (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
  • Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
Keywords
asymptotic covariance matrix

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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