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dc.contributor.authorKuswanto, Herien_US
dc.contributor.authorSibbertsen, Philippen_US
dc.date.accessioned2007-11-20en_US
dc.date.accessioned2009-08-06T13:11:11Z-
dc.date.available2009-08-06T13:11:11Z-
dc.date.issued2007en_US
dc.identifier.urihttp://hdl.handle.net/10419/27190-
dc.description.abstractWe show that specific nonlinear time series models such as SETAR, LSTAR, ESTAR and Markov switching which are common in econometric practice can hardly be distinguished from long memory by standard methods such as the GPH estimator for the memory parameter or linearity tests either general or against a specific nonlinear model. We show by Monte Carlo that under certain conditions, the nonlinear data generating process can have misleading either stationary or non-stationary long memory properties.en_US
dc.language.isoengen_US
dc.publisherFachbereich Wirtschaftswiss., Univ. Hannoveren_US
dc.relation.ispartofseriesDiscussion papers // School of Economics and Management of the Hanover Leibniz University 380en_US
dc.subject.jelC12en_US
dc.subject.jelC22en_US
dc.subject.ddc330en_US
dc.subject.keywordNonlinear modelsen_US
dc.subject.keywordlong - range dependenciesen_US
dc.subject.stwZeitreihenanalyseen_US
dc.subject.stwNichtlineares Verfahrenen_US
dc.subject.stwTheorieen_US
dc.titleCan we distinguish between common nonlinear time series models and long memory?en_US
dc.typeWorking Paperen_US
dc.identifier.ppn549847294en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
Appears in Collections:Diskussionspapiere, Wirtschaftswissenschaftliche Fakultät, Universität Hannover

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