Testing for stationarity of functional time series in the frequency domain

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dc.contributor.authorAue, Alexander
dc.contributor.authorvan Delft, Anne
dc.date.accessioned2019-09-06T13:27:32Z
dc.date.available2019-09-06T13:27:32Z
dc.date.issued2019
dc.description.abstractInterest 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.en
dc.identifier.urihttp://hdl.handle.net/2003/38206
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-20185
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;19/2019
dc.subjectfrequency domain methodsen
dc.subjectspectral analysisen
dc.subjectlocally stationary processesen
dc.subjectfunctional data analysisen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleTesting for stationarity of functional time series in the frequency domainen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

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