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Testing for a break in persistence under long-range dependencies and mean shifts

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Author Info
Sibbertsen, Philipp
Willert, Juliane

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Abstract

We show that the CUSUM-squared based test for a change in persistence by Leybourne et al. (2007) is not robust against shifts in the mean. A mean shift leads to serious size distortions. Therefore, adjusted critical values are needed when it is known that the data generating process has a mean shift. These are given for the case of one mean break. Response curves for the critical values are derived and a Monte Carlo study showing the size and power properties under this general de-trending is given

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Publisher Info
Paper provided by Universität Hannover, Wirtschaftswissenschaftliche Fakultät in its series Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Universität Hannover with number dp-422.

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Length: 13 pages
Date of creation: Jul 2009
Date of revision:
Handle: RePEc:han:dpaper:dp-422

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Related research
Keywords: Break in persistence; long memory; structural break; level shift;

Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

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References listed on IDEAS
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.:
  1. Belaire-Franch, Jorge, 2005. "A Proof Of The Power Of Kim'S Test Against Stationary Processes With Structural Breaks," Econometric Theory, Cambridge University Press, vol. 21(06), pages 1172-1176, December. [Downloadable!]
  2. Stephen Leybourne & Robert Taylor & Tae-Hwan Kim, 2007. "CUSUM of Squares-Based Tests for a Change in Persistence," Journal of Time Series Analysis, Blackwell Publishing, vol. 28(3), pages 408-433, 05. [Downloadable!] (restricted)
  3. Kim, Jae-Young, 2000. "Detection of change in persistence of a linear time series," Journal of Econometrics, Elsevier, vol. 95(1), pages 97-116, March. [Downloadable!] (restricted)
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This page was last updated on 2009-12-3.

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