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Mean Shift detection under long-range dependencies with ART

Autor: Juliane Willert
Nummer: 437, Feb 2010, pp. 14
JEL-Class: C14, C22

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.


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