Mean shift detection under long-range dependencies with ART - Juliane Willert (Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, 2010)
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: C14; C22; 330; long memory; mean shift; regression tree; ART; BIC; Zeitreihenanalyse; Maßzahl; Regression; Theorie