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

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. Copyright 2005 Blackwell Publishing Ltd.