Generalized Least Squares Estimation for Cointegration Parameters Under Conditional Heteroskedasticity

Helmut Herwartz and Helmut Luetkepohl
Authors registered in the RePEc Author Service: Helmut Lütkepohl

No ECO2009/42, Economics Working Papers from European University Institute

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.

Keywords: Vector autoregressive process; vector error correction model; cointegration; reduced rank estimation; maximum likelihood estimation; multivariate GARCH (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
Date: 2009
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Journal Article: Generalized least squares estimation for cointegration parameters under conditional heteroskedasticity (2011)
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