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Estimation of cyclic long‐memory parameters

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  • Huda Mohammed Alomari
  • Antoine Ayache
  • Myriam Fradon
  • Andriy Olenko

Abstract

This paper studies cyclic long‐memory processes with Gegenbauer‐type spectral densities. For a semiparametric statistical model, new simultaneous estimates for singularity location and long‐memory parameters are proposed. This generalized filtered method‐of‐moments approach is based on general filter transforms that include wavelet transformations as a particular case. It is proved that the estimates are almost surely convergent to the true values of parameters. Solutions of the estimation equations are studied, and adjusted statistics are proposed. Monte‐Carlo study results are presented to confirm the theoretical findings.

Suggested Citation

  • Huda Mohammed Alomari & Antoine Ayache & Myriam Fradon & Andriy Olenko, 2020. "Estimation of cyclic long‐memory parameters," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(1), pages 104-133, March.
    • Handle: RePEc:bla:scjsta:v:47:y:2020:i:1:p:104-133
    DOI: 10.1111/sjos.12404
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