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Semiparametric detection of changes in long range dependence

Author

Listed:
  • Fabrizio Iacone

    (University of York)

  • Stepana Lazarova

    (Queen Mary University of London)

Abstract

We consider changes in the degree of persistence of a process when the degree of persistence is characterized as the order of integration of a strongly dependent process. To avoid the risk of incorrectly specifying the data generating process we employ local Whittle estimates which uses only frequencies local at zero. The limit distribution of the test statistic under the null is not standard but it is well known in the literature. A Monte Carlo study shows that this inference procedure performs well in finite samples.

Suggested Citation

  • Fabrizio Iacone & Stepana Lazarova, 2017. "Semiparametric detection of changes in long range dependence," Working Papers 830, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:830
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    File URL: https://www.qmul.ac.uk/sef/media/econ/research/workingpapers/2017/items/wp830.pdf
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    Cited by:

    1. Zhanshou Chen & Yanting Xiao & Fuxiao Li, 2021. "Monitoring memory parameter change-points in long-memory time series," Empirical Economics, Springer, vol. 60(5), pages 2365-2389, May.

    More about this item

    Keywords

    Long memory; persistence; break; local Whittle estimate;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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