Authors: Aue, Alexander
van Delft, Anne
Title: Testing for stationarity of functional time series in the frequency domain
Language (ISO): en
Abstract: Interest in functional time series has spiked in the recent past with papers covering both methodology and applications being published at a much increased pace. This article contributes to the research in this area by proposing a new stationarity test for functional time series based on frequency domain methods. The proposed test statistics is based on joint dimension reduction via functional principal components analysis across the spectral density operators at all Fourier frequencies, explicitly allowing for frequency-dependent levels of truncation to adapt to the dynamics of the underlying functional time series. The properties of the test are derived both under the null hypothesis of stationary functional time series and under the smooth alternative of locally stationary functional time series. The methodology is theoretically justified through asymptotic results. Evidence from simulation studies and an application to annual temperature curves suggests that the test works well in finite samples.
Subject Headings: frequency domain methods
spectral analysis
locally stationary processes
functional data analysis
URI: http://hdl.handle.net/2003/38206
http://dx.doi.org/10.17877/DE290R-20185
Issue Date: 2019
Appears in Collections:Sonderforschungsbereich (SFB) 823

Files in This Item:
File Description SizeFormat 
DP_1919_SFB823_Aue_vanDelft.pdfDNB1.07 MBAdobe PDFView/Open


This item is protected by original copyright



All resources in the repository are protected by copyright.