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dc.contributor.authorBeran, Jande
dc.contributor.authorFeng, Yuanhuade
dc.contributor.authorOcker, Dirkde
dc.date.accessioned2004-12-06T18:39:40Z-
dc.date.available2004-12-06T18:39:40Z-
dc.date.issued1999de
dc.identifier.urihttp://hdl.handle.net/2003/4919-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-6932-
dc.description.abstractRecent results on so-called SEMIFAR models introduced by Beran (1997) are discussed. The nonparametric deterministic trend is estimated by a kernel method. The differencing- and fractional differencing parameters as well as the autoregressive coefficients are estimated by an approximate maximum likelihood approach. A data-driven algorithm for estimating the whole model is proposed based on the iterative plug-in idea for selecting bandwidth in nonparametric regression with long-memory. Prediction for SEMIFAR models is also discussed briefly. Two examples illustrate the potential usefulness of these models in practice.en
dc.format.extent237934 bytes-
dc.format.extent580150 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/postscript-
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjectbandwidthen
dc.subjectBICde
dc.subjectdifference stationarityen
dc.subjectdifferencingen
dc.subjectforecastingen
dc.subjectfractional ARIMAen
dc.subjectkernel estimationen
dc.subjectlong-range dependenceen
dc.subjectsemiparametric modelsen
dc.subjecttrenden
dc.subject.ddc310de
dc.titleSEMIFAR modelsen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 475

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