Estimation methods for the LRD parameter under a change in the mean

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dc.contributor.authorRooch, Aeneas
dc.contributor.authorZelo, Ieva
dc.contributor.authorFried, Roland
dc.date.accessioned2016-06-28T14:50:45Z
dc.date.available2016-06-28T14:50:45Z
dc.date.issued2016
dc.description.abstractWhen analyzing time series which are supposed to exhibit long-range dependence (LRD), a basic issue is the estimation of the LRD parameter, for example the Hurst parameter H 2 (1=2; 1). Conventional estimators of H easily lead to spurious detection of long memory if the time series includes a shift in the mean. This defect has fatal consequences in change-point problems: Tests for a level shift rely on H, which needs to be estimated before, but this estimation is distorted by the level shift. We investigate two blocks approaches to adapt estimators of H to the case that the time series includes a jump and compare them with other natural techniques as well as with estimators based on the trimming idea via simulations. These techniques improve the estimation of H if there is indeed a change in the mean. In the absence of such a change, the methods little affect the usual estimation. As adaption, we recommend an overlapping blocks approach: If one uses a consistent estimator, the adaption will preserve this property and it performs well in simulations.en
dc.identifier.urihttp://hdl.handle.net/2003/35124
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-17171
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;32, 2016en
dc.subjectHurst parameteren
dc.subjectchange-point problemsen
dc.subjectlong memoryen
dc.subjectlong-range dependenceen
dc.subjectjumpen
dc.subjectestimationen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleEstimation methods for the LRD parameter under a change in the meanen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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