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TESTING FOR CHANGES IN KENDALL’S TAU

Published online by Cambridge University Press:  04 November 2016

Herold Dehling ,
Daniel Vogel ,
Martin Wendler  and
Dominik Wied
Herold Dehling
Affiliation:
University of Bochum
Daniel Vogel*
Affiliation:
University of Aberdeen
Martin Wendler
Affiliation:
University of Greifswald
Dominik Wied
Affiliation:
University of Cologne
*
*Address correspondence to Daniel Vogel, Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, UK; e-mail: daniel.vogel@abdn.ac.uk.
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Abstract

For a bivariate time series ((Xi ,Yi))i=1,...,n, we want to detect whether the correlation between Xi and Yi stays constant for all i = 1,...n. We propose a nonparametric change-point test statistic based on Kendall’s tau. The asymptotic distribution under the null hypothesis of no change follows from a new U-statistic invariance principle for dependent processes. Assuming a single change-point, we show that the location of the change-point is consistently estimated. Kendall’s tau possesses a high efficiency at the normal distribution, as compared to the normal maximum likelihood estimator, Pearson’s moment correlation. Contrary to Pearson’s correlation coefficient, it shows no loss in efficiency at heavy-tailed distributions, and is therefore particularly suited for financial data, where heavy tails are common. We assume the data ((Xi ,Yi))i=1,...,n to be stationary and P-near epoch dependent on an absolutely regular process. The P-near epoch dependence condition constitutes a generalization of the usually considered Lp-near epoch dependence allowing for arbitrarily heavy-tailed data. We investigate the test numerically, compare it to previous proposals, and illustrate its application with two real-life data examples.

Type
ARTICLES
Information
Econometric Theory , Volume 33 , Issue 6 , December 2017 , pp. 1352 - 1386
Copyright
Copyright © Cambridge University Press 2016 

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Footnotes

The authors wish to thank their colleague Roland Fried for several very stimulating discussions that motivated this paper. Moreover, we are grateful for helpful comments from the editors and referees, which substantially improved a previous version of the paper. We are also indebted to Alexander Dürre, who did a thorough proofreading of the manuscript. The research was supported in part by the Collaborative Research Grant 823 Statistical modelling of nonlinear dynamic processes of the German Research Foundation.

References

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