Spectral density estimation for nonstationary data with nonzero mean function

Abstract : We introduce a new approach for nonparametric spectral density estimation based on the subsampling technique, which we apply to the important class of nonstation-ary time series. These are almost periodically correlated sequences. In contrary to existing methods our technique does not require demeaning of the data. On the simulated data examples we compare our estimator of spectral density function with the classical one. Additionally, we propose a modified estimator, which allows to reduce the leakage effect. Moreover, we provide two real data economic applications.
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Pré-publication, Document de travail
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https://hal.archives-ouvertes.fr/hal-02442913
Contributeur : Anna Dudek <>
Soumis le : jeudi 16 janvier 2020 - 18:15:33
Dernière modification le : mardi 21 janvier 2020 - 01:31:11

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  • HAL Id : hal-02442913, version 1

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Anna E. Dudek, Lukasz Lenart. Spectral density estimation for nonstationary data with nonzero mean function. 2020. ⟨hal-02442913⟩

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