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Fractional Cointegrating Regression In The Presence Of Linear Time Trends

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Author Info
Uwe Hassler (Free University of Berlin)
Francesc Marmol (University Carlos III of Madrid)
C. Velasco (University Carlos III of Madrid)

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Abstract

We consider regressions of nonstationary fractionally integrated variables dominated by linear time trends. The regression errors can be short memory, long memory, or even nonstationary, and hence allow for a very flexible cointegration model. Our main contributions are two: First, we analyze the limiting behaviour of the regression estimators. We find in case of simple regressions that limiting normality arises at a rate of convergence that is independent of the order of integration of the regressor. This result does not carry over to the multivariate case, where the limiting distribution is more complicated. Second, we investigate a residual-based, log-periodogram regression. We state conditions that allow consistent estimation of the memory parameter of the error term. This estimator follows a limiting normal distribution and is therefore suitable for cointegration testing. The applicability of this asymptotic result to finite samples is established by means of Monte Carlo experiments.

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Publisher Info
Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 138.

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Date of creation: 05 Jul 2000
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Handle: RePEc:sce:scecf0:138

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  1. Katarzyna Lasak, 2008. "Maximum likelihood estimation of fractionally cointegrated systems," CREATES Research Papers 2008-53, School of Economics and Management, University of Aarhus. [Downloadable!]
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