Spectral Density Estimation for Nonstationary Data With Nonzero Mean Function

• 成果类型: Article
• 署名作者: Dudek, Anna E.; Lenart, Lukasz
• 署名单位: AGH University of Krakow; Cracow University of Economics
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.2021919
• 发表日期: 2023
• 页码: 1900-1910
• 关键词: block bootstrap; time-series; cyclostationarity

摘要

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 , we provide a simulation study and two real data economic applications. for this article are available online.