Mean Shift detection under long-range dependencies with ART

Juliane Willert

MPRA Paper from University Library of Munich, Germany

Abstract: Atheoretical regression trees (ART) are applied to detect changes in the mean of a stationary long memory time series when location and number are unknown. It is shown that the BIC, which is almost always used as a pruning method, does not operate well in the long memory framework. A new method is developed to determine the number of mean shifts. A Monte Carlo Study and an application is given to show the performance of the method.

Keywords: long memory; mean shift; regression tree; ART; BIC (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Date: 2009-07-06
New Economics Papers: this item is included in nep-ecm and nep-ets
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https://mpra.ub.uni-muenchen.de/17874/1/MPRA_paper_17874.pdf original version (application/pdf)

Related works:
Working Paper: Mean Shift detection under long-range dependencies with ART (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:17874

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