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 Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät in its series Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Leibniz Universität Hannover with number dp-437.
Length: 14 pages
Date of creation: Feb 2010
Date of revision:
long memory; mean shift; regression tree; ART; BIC.;
Other versions of this item:
- Willert, Juliane, 2009. "Mean Shift detection under long-range dependencies with ART," MPRA Paper 17874, University Library of Munich, Germany.
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-02-13 (All new papers)
- NEP-ECM-2010-02-13 (Econometrics)
- NEP-ETS-2010-02-13 (Econometric Time Series)
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