Abstract
This study examines the problem of robust spectral analysis of long-memory processes. We investigate the possibility of using Laplace and quantile periodograms for a non-Gaussian distribution structure. The Laplace periodogram, derived by the least absolute deviations in the harmonic regression procedure, demonstrates its superiority in handling heavy-tailed noise and nonlinear distortion. In this study, we discuss an asymptotic distribution of the Laplace periodogram for long-memory processes. We also derive an asymptotic distribution of the quantile periodogram. Through numerical experiments, we demonstrate the robustness of the Laplace periodogram and the usefulness of the quantile periodogram in detecting the hidden frequency for the spectral analysis of the long-memory process under non-Gaussian distribution. Moreover, as an application of robust periodograms under the long-memory process, we discuss the long-memory parameter estimation based on a log periodogram regression approach.
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Funding
This study was supported by the National Research Foundation of Korea (NRF) funded by the Korea government (2018R1D1A1B07042933, 2019R1A2C4069453, 2020R1A4A1018207).
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Lim, Y., Oh, HS. Quantile spectral analysis of long-memory processes. Empir Econ 62, 1245–1266 (2022). https://doi.org/10.1007/s00181-021-02045-z
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DOI: https://doi.org/10.1007/s00181-021-02045-z