Practical Problems with Reduced Rank ML Estimators for Cointegration Parameters and a Simple Alternative
AbstractJohansen'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.
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Date of creation: 2004
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Vector autoregressive process; Vector error correction model; Cointegration; Reduced rank estimation; Maximum likelihood estimation;
Other versions of this item:
- Ralf Brüggemann & Helmut Lütkepohl, 2005. "Practical Problems with Reduced-rank ML Estimators for Cointegration Parameters and a Simple Alternative," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(5), pages 673-690, October.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-06-27 (All new papers)
- NEP-ECM-2004-06-27 (Econometrics)
- NEP-ETS-2004-06-27 (Econometric Time Series)
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