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A General Frequency Domain Estimation Method for Gegenbauer Processes

Author

Listed:
  • Hunt Richard

    (The University of Sydney, School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, 2006Australia)

  • Peiris Shelton

    (The University of Sydney, School of Mathematics and Statistics, Sydney, New South Wales, Australia)

  • Weber Neville

    (The University of Sydney, School of Mathematics and Statistics, Sydney, New South Wales, Australia)

Abstract

In this paper a new method for estimation of all the parameters of a k-factor Gegenbauer process is developed using a broadband nonlinear least-squares regression technique in the frequency-domain, with similarities to a Whittle estimator. Simulation studies where the underlying distribution is symmetric suggest that while the new method may have a slightly lower level of accuracy than existing methods (Whittle, conditional sum-of-squares), it can improve the accuracy in determining the values for the short-memory parameters of highly skewed non-Gaussian data (e.g., χ2), while having the added advantage of being evaluated considerably faster. In a supplementary addendum we provide some theoretical results under a Gaussian assumption.

Suggested Citation

  • Hunt Richard & Peiris Shelton & Weber Neville, 2021. "A General Frequency Domain Estimation Method for Gegenbauer Processes," Journal of Time Series Econometrics, De Gruyter, vol. 13(2), pages 119-144, July.
    • Handle: RePEc:bpj:jtsmet:v:13:y:2021:i:2:p:119-144:n:4
    DOI: 10.1515/jtse-2019-0031
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    Cited by:

    1. Richard Hunt & Shelton Peiris & Neville Weber, 2022. "Estimation methods for stationary Gegenbauer processes," Statistical Papers, Springer, vol. 63(6), pages 1707-1741, December.

    More about this item

    Keywords

    GARMA; Gegenbauer;

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