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A Fast Algorithm for the Computation of HAC Covariance Matrix Estimators

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  • Jochen Heberle

    (Faculty of Business Administration, Universität Hamburg, 20146 Hamburg, Germany)

  • Cristina Sattarhoff

    (Faculty of Business Administration, Universität Hamburg, 20146 Hamburg, Germany)

Abstract

This paper considers the algorithmic implementation of the heteroskedasticity and autocorrelation consistent (HAC) estimation problem for covariance matrices of parameter estimators. We introduce a new algorithm, mainly based on the fast Fourier transform, and show via computer simulation that our algorithm is up to 20 times faster than well-established alternative algorithms. The cumulative effect is substantial if the HAC estimation problem has to be solved repeatedly. Moreover, the bandwidth parameter has no impact on this performance. We provide a general description of the new algorithm as well as code for a reference implementation in R .

Suggested Citation

  • Jochen Heberle & Cristina Sattarhoff, 2017. "A Fast Algorithm for the Computation of HAC Covariance Matrix Estimators," Econometrics, MDPI, vol. 5(1), pages 1-16, January.
    • Handle: RePEc:gam:jecnmx:v:5:y:2017:i:1:p:9-:d:88731
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    References listed on IDEAS

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