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Fourier Analysis of Serial Dependence Measures
Author(s) -
Van Hecke Ria,
Volgushev Stanislav,
Dette Holger
Publication year - 2018
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12266
Subject(s) - mathematics , limiting , fourier transform , statistics , variance (accounting) , frequency domain , econometrics , spectral density , discrete fourier transform (general) , statistical physics , fourier analysis , spectral analysis , mathematical analysis , short time fourier transform , physics , economics , mechanical engineering , accounting , engineering , quantum mechanics , spectroscopy
Classical spectral analysis is based on the discrete Fourier transform of the autocovariances. In this article we investigate the asymptotic properties of new frequency‐domain methods where the autocovariances in the spectral density are replaced by alternative dependence measures that can be estimated by U ‐statistics. An interesting example is given by Kendall's τ , for which the limiting variance exhibits a surprising behavior.