Autor(en): Preuß, Philip
Sen, Kemal
Dette, Holger
Titel: Detecting long-range dependence in non-stationary time series
Sprache (ISO): en
Zusammenfassung: An important problem in time series analysis is the discrimination between non-stationarity and longrange dependence. Most of the literature considers the problem of testing specificc parametric hypotheses of non-stationarity (such as a change in the mean) against long-range dependent stationary alternatives. In this paper we suggest a simple nonparametric approach, which can be used to test the null-hypothesis of a general non-stationary short-memory against the alternative of a non-stationary long-memory process. This test is working in the spectral domain and uses a sieve of approximating tvFARIMA models to estimate the time varying long-range dependence parameter nonparametrically. We prove uniform consistency of this estimate and asymptotic normality of an averaged version. These results yield a simple test (based on the quantiles of the standard normal distribution), and it is demonstrated in a simulation study that - despite of its nonparametric nature - the new test outperforms the currently available methods, which are constructed to discriminate between speci fic parametric hypotheses of non-stationarity short- and stationarity long-range dependence.
Schlagwörter: spectral density
sieve method
locally stationary process
integrated periodogram
empirical spectral measure
goodness-of- fit tests
non-stationary processes
long-memory
URI: http://hdl.handle.net/2003/31550
http://dx.doi.org/10.17877/DE290R-13182
Erscheinungsdatum: 2013-12-18
Enthalten in den Sammlungen:Sonderforschungsbereich (SFB) 823

Dateien zu dieser Ressource:
Datei Beschreibung GrößeFormat 
DP_5013_SFB823_Preuß_Sen_Dette.pdfDNB508.97 kBAdobe PDFÖffnen/Anzeigen


Diese Ressource ist urheberrechtlich geschützt.



Diese Ressource ist urheberrechtlich geschützt. rightsstatements.org