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Mean Shift detection under long-range dependencies with ART

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Author Info
Willert, Juliane

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Abstract

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.

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File URL: http://mpra.ub.uni-muenchen.de/17874/
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Publisher Info
Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 17874.

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Date of creation: 06 Jul 2009
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Handle: RePEc:pra:mprapa:17874

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Related research
Keywords: long memory; mean shift; regression tree; ART; BIC;

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

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  1. Clive W.J. Granger & Namwon Hyung, 1999. "Occasional Structural Breaks and Long Memory," University of California at San Diego, Economics Working Paper Series 99-14, Department of Economics, UC San Diego. [Downloadable!]
    Other versions:
  2. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November. [Downloadable!] (restricted)
    Other versions:
  3. Sandrine Corvoisier & Benoît Mojon, 2005. "Breaks in the mean of inflation - how they happen and what to do with them," Working Paper Series 451, European Central Bank. [Downloadable!]
  4. Ploberger, Werner & Kramer, Walter, 1992. "The CUSUM Test with OLS Residuals," Econometrica, Econometric Society, vol. 60(2), pages 271-85, March. [Downloadable!] (restricted)
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This page was last updated on 2009-12-6.

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