Mean Shift detection under long-range dependencies with ART
AbstractAtheoretical 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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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 17874.
Date of creation: 06 Jul 2009
Date of revision:
long memory; mean shift; regression tree; ART; BIC;
Other versions of this item:
- Willert, Juliane, 2010. "Mean Shift detection under long-range dependencies with ART," Diskussionspapiere der Wirtschaftswissenschaftlichen FakultÃ¤t der Leibniz UniversitÃ¤t Hannover dp-437, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-10-24 (All new papers)
- NEP-ECM-2009-10-24 (Econometrics)
- NEP-ETS-2009-10-24 (Econometric Time Series)
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