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Fourier Analysis of Serial Dependence Measures

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  • Ria Van Hecke
  • Stanislav Volgushev
  • Holger Dette

Abstract

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.

Suggested Citation

  • Ria Van Hecke & Stanislav Volgushev & Holger Dette, 2018. "Fourier Analysis of Serial Dependence Measures," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(1), pages 75-89, January.
  • Handle: RePEc:bla:jtsera:v:39:y:2018:i:1:p:75-89
    DOI: 10.1111/jtsa.12266
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    Cited by:

    1. Ta‐Hsin Li, 2021. "Quantile‐frequency analysis and spectral measures for diagnostic checks of time series with nonlinear dynamics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 270-290, March.
    2. Nasri, Bouchra R., 2022. "Tests of serial dependence for multivariate time series with arbitrary distributions," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    3. Ta-Hsin Li, 2019. "Quantile-Frequency Analysis and Spectral Divergence Metrics for Diagnostic Checks of Time Series With Nonlinear Dynamics," Papers 1908.02545, arXiv.org.

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