Generalized Least Squares Estimation for Cointegration Parameters Under Conditional Heteroskedasticity

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dc.contributor.author HERWARTZ, Helmut
dc.contributor.author LUETKEPOHL, Helmut
dc.date.accessioned 2009-12-11T15:07:58Z
dc.date.available 2009-12-11T15:07:58Z
dc.date.issued 2009
dc.identifier.issn 1725-6704
dc.identifier.uri http://hdl.handle.net/1814/12965
dc.description.abstract In 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.language.iso en en
dc.relation.ispartofseries EUI ECO en
dc.relation.ispartofseries 2009/42 en
dc.subject Vector autoregressive process en
dc.subject vector error correction model en
dc.subject cointegration en
dc.subject reduced rank estimation en
dc.subject maximum likelihood estimation en
dc.subject multivariate GARCH en
dc.subject C32 en
dc.title Generalized Least Squares Estimation for Cointegration Parameters Under Conditional Heteroskedasticity en
dc.type Working Paper en
dc.neeo.contributor HERWARTZ|Helmut|aut|
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