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dc.contributor.authorHERWARTZ, Helmut
dc.contributor.authorLUETKEPOHL, Helmut
dc.date.accessioned2009-12-11T15:07:58Z
dc.date.available2009-12-11T15:07:58Z
dc.date.issued2009
dc.identifier.issn1725-6704
dc.identifier.urihttp://hdl.handle.net/1814/12965
dc.description.abstractIn the presence of generalized conditional heteroscedasticity (GARCH) in the residuals of a vector error correction model (VECM), maximum likelihood (ML) estimation of the cointegration parameters has been shown to be efficient. On the other hand, full ML estimation of VECMs with GARCH residuals is computationally di±cult and may not be feasible for larger models. Moreover, ML estimation of VECMs with independently identically distributed residuals is known to have potentially poor small sample properties and this problem also persists when there are GARCH residuals. A further disadvantage of the ML estimator is its sensitivity to misspecification of the GARCH process. We propose a feasible generalized least squares estimator which addresses all these problems. It is easy to compute and has superior small sample properties in the presence of GARCH residuals.en
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.relation.ispartofseriesEUI ECOen
dc.relation.ispartofseries2009/42en
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectVector autoregressive processen
dc.subjectvector error correction modelen
dc.subjectcointegrationen
dc.subjectreduced rank estimationen
dc.subjectmaximum likelihood estimationen
dc.subjectmultivariate GARCHen
dc.subjectC32en
dc.titleGeneralized Least Squares Estimation for Cointegration Parameters Under Conditional Heteroskedasticityen
dc.typeWorking Paperen
dc.neeo.contributorHERWARTZ|Helmut|aut|
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