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Improved HAC covariance matrix estimation based on forecast errors

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  • Kuan, Chung-Ming
  • Hsieh, Yu-Wei

Abstract

We propose computing HAC covariance matrix estimators based on one-step-ahead forecasting errors. It is shown that this estimator is consistent and has smaller bias than other HAC estimators. Moreover, the tests that rely on this estimator have more accurate sizes without sacrificing its power.

Suggested Citation

  • Kuan, Chung-Ming & Hsieh, Yu-Wei, 2008. "Improved HAC covariance matrix estimation based on forecast errors," Economics Letters, Elsevier, vol. 99(1), pages 89-92, April.
  • Handle: RePEc:eee:ecolet:v:99:y:2008:i:1:p:89-92
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    6. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
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    Cited by:

    1. Hartigan, Luke, 2018. "Alternative HAC covariance matrix estimators with improved finite sample properties," Computational Statistics & Data Analysis, Elsevier, vol. 119(C), pages 55-73.

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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