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 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 &bull Diffusion Processes
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)
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.:
- Ploberger, Werner & Kramer, Walter, 1992. "The CUSUM Test with OLS Residuals," Econometrica, Econometric Society, vol. 60(2), pages 271-85, March.
- Francis X. Diebold & Atsushi Inoue, 2000.
"Long Memory and Regime Switching,"
NBER Technical Working Papers
0264, National Bureau of Economic Research, Inc.
- Philipp Sibbertsen, 2004.
"Long memory versus structural breaks: An overview,"
Springer, vol. 45(4), pages 465-515, October.
- Clive W.J. Granger & Namwon Hyung, 2013.
"Occasional Structural Breaks and Long Memory,"
Annals of Economics and Finance,
Society for AEF, vol. 14(3), pages 739-764, December.
- Granger, Clive W.J. & Hyung, Namwon, 1999. "Occasional Structural Breaks and Long Memory," University of California at San Diego, Economics Working Paper Series 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 0451, European Central Bank.
- da Rosa, Joel Correa & Veiga, Alvaro & Medeiros, Marcelo C., 2008. "Tree-structured smooth transition regression models," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2469-2488, January.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dietrich, Karl).
If references are entirely missing, you can add them using this form.