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Asymptotic normality of simultaneous estimators of cyclic long-memory processes

Statistics Theory 2020-11-13 v1 Probability Statistics Theory

Abstract

Spectral singularities at non-zero frequencies play an important role in investigating cyclic or seasonal time series. The publication [2] introduced the generalized filtered method-of-moments approach to simultaneously estimate singularity location and long-memory parameters. This paper continues studies of these simultaneous estimators. A wide class of Gegenbauer-type semi-parametric models is considered. Asymptotic normality of several statistics of the cyclic and long-memory parameters is proved. New adjusted estimates are proposed and investigated. The theoretical findings are illustrated by numerical results. The methodology includes wavelet transformations as a particular case.

Keywords

Cite

@article{arxiv.2011.06229,
  title  = {Asymptotic normality of simultaneous estimators of cyclic long-memory processes},
  author = {Antoine Ayache and Myriam Fradon and Ravindi Nanayakkara and Andriy Olenko},
  journal= {arXiv preprint arXiv:2011.06229},
  year   = {2020}
}

Comments

30 pages, 4 figures