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dc.contributor.authorMEITZ, Mika
dc.contributor.authorSAIKKONEN, Pentti
dc.date.accessioned2008-06-05T08:46:49Z
dc.date.available2008-06-05T08:46:49Z
dc.date.issued2008
dc.identifier.issn1725-6704
dc.identifier.urihttp://hdl.handle.net/1814/8770
dc.description.abstractThis paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a functional coe cient autoregression of order p (AR(p)) with the conditional variance specified as a general nonlinear fist order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. Strong consistency and asymptotic normality of the global Gaussian quasi maximum likelihood (QML) estimator are established under conditions comparable to those recently used in the corresponding linear case. To the best of our knowledge, this paper provides the first results on consistency and asymptotic normality of the QML estimator in nonlinear autoregressive models with GARCH errors.en
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherEuropean University Institute
dc.relation.ispartofseriesEUI ECOen
dc.relation.ispartofseries2008/25en
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectC13en
dc.subjectC22en
dc.titleParameter Estimation in Nonlinear AR-GARCH Modelsen
dc.typeWorking Paperen
dc.neeo.contributorMEITZ|Mika|aut|
dc.neeo.contributorSAIKKONEN|Pentti|aut|
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