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