Kendall's tau for serial dependence

Marc Hallin, Thomas S. Ferguson and Christian Genest

ULB Institutional Repository from ULB -- Universite Libre de Bruxelles

Abstract: The authors show how Kendall's tau can be adapted to test against serial dependence in a univariate time series context. They provide formulas for the mean and variance of circular and noncircular versions of this statistic, and they prove its asymptotic normality under the hypothesis of independence. They present also a Monte Carlo study comparing the power and size of a test based on Kendall's tau with the power and size of competing procedures based on alternative parametric and nonparametric measures of serial dependence. In particular, their simulations indicate that Kendall's tau outperforms Spearman's rho in detecting first-order autoregressive dependence, despite the fact that these two statistics are asymptotically equivalent under the null hypothesis, as well as under local alternatives.

Date: 2000-09
Note: FLWIN
References: Add references at CitEc
Citations: View citations in EconPapers (15)

Published in: Canadian Journal of Statistics (2000) v.28 n° 3,p.587-604

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ulb:ulbeco:2013/2093

Ordering information: This working paper can be ordered from
http://hdl.handle.ne ... .ulb.ac.be:2013/2093

Access Statistics for this paper

More papers in ULB Institutional Repository from ULB -- Universite Libre de Bruxelles Contact information at EDIRC.
Bibliographic data for series maintained by Benoit Pauwels ().