Parameter Estimation in Nonlinear AR-GARCH Models

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dc.contributor.author MEITZ, Mika
dc.contributor.author SAIKKONEN, Pentti
dc.date.accessioned 2008-06-05T08:46:49Z
dc.date.available 2008-06-05T08:46:49Z
dc.date.issued 2008
dc.identifier.issn 1725-6704
dc.identifier.uri http://hdl.handle.net/1814/8770
dc.description.abstract This 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.language.iso en en
dc.publisher European University Institute
dc.relation.ispartofseries EUI ECO en
dc.relation.ispartofseries 2008/25 en
dc.subject C13 en
dc.subject C22 en
dc.title Parameter Estimation in Nonlinear AR-GARCH Models en
dc.type Working Paper en
dc.neeo.contributor MEITZ|Mika|aut|
dc.neeo.contributor SAIKKONEN|Pentti|aut|
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