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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
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," Hannover Economic Papers (HEP), Leibniz UniversitÃ¤t Hannover, Wirtschaftswissenschaftliche FakultÃ¤t 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)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Francis X. Diebold & Atsushi Inoue, 2000.
"Long Memory and Regime Switching,"
NBER Technical Working Papers, National Bureau of Economic Research, Inc
0264, National Bureau of Economic Research, Inc.
- Philipp Sibbertsen, 2004.
"Long memory versus structural breaks: An overview,"
Statistical Papers, Springer,
Springer, vol. 45(4), pages 465-515, October.
- Sibbertsen, Philipp, 2001. "Long-memory versus structural breaks: An overview," Technical Reports, Technische UniversitÃ¤t Dortmund, Sonderforschungsbereich 475: KomplexitÃ¤tsreduktion in multivariaten Datenstrukturen 2001,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
- da Rosa, Joel Correa & Veiga, Alvaro & Medeiros, Marcelo C., 2008. "Tree-structured smooth transition regression models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 52(5), pages 2469-2488, January.
- Ploberger, Werner & Kramer, Walter, 1992. "The CUSUM Test with OLS Residuals," Econometrica, Econometric Society, Econometric Society, vol. 60(2), pages 271-85, March.
- Clive W.J. Granger & Namwon Hyung, 2013.
"Occasional Structural Breaks and Long Memory,"
Annals of Economics and Finance, Society for AEF,
Society for AEF, vol. 14(2), pages 739-764, November.
- Granger, Clive W.J. & Hyung, Namwon, 1999. "Occasional Structural Breaks and Long Memory," University of California at San Diego, Economics Working Paper Series, Department of Economics, UC San Diego qt4d60t4jh, Department of Economics, UC San Diego.
- Corvoisier, Sandrine & Mojon, Benoît, 2005. "Breaks in the mean of inflation: how they happen and what to do with them," Working Paper Series, European Central Bank 0451, European Central Bank.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ekkehart Schlicht).
If references are entirely missing, you can add them using this form.