Parameter Estimation in Nonlinear AR-GARCH Models

DSpace/Manakin Repository

Show simple item record MEITZ, Mika SAIKKONEN, Pentti 2008-06-05T08:46:49Z 2008-06-05T08:46:49Z 2008
dc.identifier.issn 1725-6704
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.format.mimetype application/pdf
dc.language.iso en en
dc.publisher European University Institute
dc.relation.ispartofseries EUI ECO en
dc.relation.ispartofseries 2008/25 en
dc.rights info:eu-repo/semantics/openAccess
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|
eui.subscribe.skip true

Files in this item

This item appears in the following Collection(s)

Show simple item record