Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Preuß, Philip | - |
dc.contributor.author | Sen, Kemal | - |
dc.contributor.author | Dette, Holger | - |
dc.date.accessioned | 2013-12-18T16:13:39Z | - |
dc.date.available | 2013-12-18T16:13:39Z | - |
dc.date.issued | 2013-12-18 | - |
dc.identifier.uri | http://hdl.handle.net/2003/31550 | - |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-13182 | - |
dc.description.abstract | 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. | en |
dc.language.iso | en | de |
dc.relation.ispartofseries | Discussion Paper / SFB 823;50/2013 | - |
dc.subject | spectral density | en |
dc.subject | sieve method | en |
dc.subject | locally stationary process | en |
dc.subject | integrated periodogram | en |
dc.subject | empirical spectral measure | en |
dc.subject | goodness-of- fit tests | en |
dc.subject | non-stationary processes | en |
dc.subject | long-memory | en |
dc.subject.ddc | 310 | - |
dc.subject.ddc | 330 | - |
dc.subject.ddc | 620 | - |
dc.title | Detecting long-range dependence in non-stationary time series | en |
dc.type | Text | de |
dc.type.publicationtype | workingPaper | de |
dcterms.accessRights | open access | - |
Appears in Collections: | Sonderforschungsbereich (SFB) 823 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
DP_5013_SFB823_Preuß_Sen_Dette.pdf | DNB | 508.97 kB | Adobe PDF | View/Open |
This item is protected by original copyright |
This item is protected by original copyright
rightsstatements.org