On filter-type estimation of discretely sampled cyclic long-memory processes

Antoine Ayache; Serhii Kravchenko; Andriy Olenko

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arXiv · 2407.12444

On filter-type estimation of discretely sampled cyclic long-memory processes

Antoine Ayache·Serhii Kravchenko·Andriy Olenko

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Abstract

The generalized filtered method of moments was developed in the recent papers by Alomari et al., 2020, and Ayache et al., 2022. It used functional data obtained from continuously sampled cyclic long-memory stochastic processes to simultaneously estimate their parameters. However, the majority of applications deal with discretely sampled processes or time series. This paper extends the approach to accommodate discrete-time scenarios. It proves that the new discrete estimates exhibit analogous properties to the continuous case and are strongly consistent with the same rates of convergence. The numerical study results are presented to illustrate the theoretical findings and to indicate the sampling rates and resolution levels required for accurate estimates.

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Antoine Ayache, Serhii Kravchenko, Andriy Olenko. 2024-07-17. On filter-type estimation of discretely sampled cyclic long-memory processes. https://arxiv.org/abs/2407.12444

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