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dc.contributor.authorKuswanto, Herien
dc.contributor.authorSibbertsen, Philippen
dc.date.accessioned2007-11-20-
dc.date.accessioned2009-08-06T13:11:11Z-
dc.date.available2009-08-06T13:11:11Z-
dc.date.issued2007-
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
dc.language.isoengen
dc.publisher|aLeibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät |cHannoveren
dc.relation.ispartofseries|aDiskussionsbeitrag |x380en
dc.subject.jelC12en
dc.subject.jelC22en
dc.subject.ddc330en
dc.subject.keywordNonlinear modelsen
dc.subject.keywordlong - range dependenciesen
dc.subject.stwZeitreihenanalyseen
dc.subject.stwNichtlineares Verfahrenen
dc.subject.stwTheorieen
dc.titleCan we distinguish between common nonlinear time series models and long memory?-
dc.type|aWorking Paperen
dc.identifier.ppn549847294en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:han:dpaper:dp-380en

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