Practical Problems with Reduced Rank ML Estimators for Cointegration Parameters and a Simple Alternative

Ralf Brueggemann and Helmut Luetkepohl
Authors registered in the RePEc Author Service: Ralf Brüggemann and Helmut Lütkepohl

No ECO2004/20, Economics Working Papers from European University Institute

Abstract: Johansen's reduced rank maximum likelihood (ML) estimator for cointegration parameters in vector error correction models is known to produce occasional extreme outliers. Using a small monetary system and German data we illustrate the practical importance of this problem. We also consider an alternative generalized least squares (GLS) system estimator which has better properties in this respect. The two estimators are compared in a small simulation study. It is found that the GLS estimator can indeed be an attractive alternative to ML estimation of cointegration parameters.

Keywords: Vector autoregressive process; Vector error correction model; Cointegration; Reduced rank estimation; Maximum likelihood estimation (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
Date: 2004
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (8)

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Journal Article: Practical Problems with Reduced‐rank ML Estimators for Cointegration Parameters and a Simple Alternative (2005)
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