Spectral Density Estimation for Nonstationary Data With Nonzero Mean Function

Anna E. Dudek, Lukasz Lenart

Research output: Contribution to journalArticlepeer-review

Abstract

We introduce a new approach for nonparametric spectral density estimation based on the subsampling technique, which we apply to the important class of nonstationary 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, in the supplementary materials, we provide a simulation study and two real data economic applications. Supplementary materials for this article are available online.
Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalJOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
DOIs
StatePublished - Jan 31 2022
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-05-25
Acknowledged KAUST grant number(s): OSR-2019-CRG8-4057.2
Acknowledgements: Anna Dudek acknowledges support from the King Abdullah University of Science and Technology (KAUST) Research Grant OSR-2019-CRG8-4057.2. Łukasz Lenart acknowledges support from a subsidy granted to Cracow University of Economics.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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