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Estimation methods for stationary Gegenbauer processes

Author

Listed:
  • Richard Hunt

    (University of Sydney)

  • Shelton Peiris

    (University of Sydney)

  • Neville Weber

    (University of Sydney)

Abstract

This paper reviews alternative methods for estimation for stationary Gegenbauer processes specifically, as distinct from the more general long memory models. A short set of Monte Carlo simulations is used to compare the accuracy of these methods. The conclusion found is that a Bayesian technique results in the highest accuracy. The paper is completed with an examination of the SILSO Sunspot Number series as collated by the Royal Observatory of Belgium.

Suggested Citation

  • Richard Hunt & Shelton Peiris & Neville Weber, 2022. "Estimation methods for stationary Gegenbauer processes," Statistical Papers, Springer, vol. 63(6), pages 1707-1741, December.
    • Handle: RePEc:spr:stpapr:v:63:y:2022:i:6:d:10.1007_s00362-022-01290-3
    DOI: 10.1007/s00362-022-01290-3
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    2. Oumaima Essefiani & Rachid El Halimi & Said Hamdoune, 2024. "Some Estimation Methods for a Random Coefficient in the Gegenbauer Autoregressive Moving-Average Model," Mathematics, MDPI, vol. 12(11), pages 1-16, May.

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