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Semiparametric Detection of Changes in Long Range Dependence

Author

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  • Fabrizio Iacone
  • Štěpána Lazarová

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 to 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. We demonstrate the practical utility of these results with an empirical example, where we analyze the inflation rate in Germany for the period 1986–2017.

Suggested Citation

  • Fabrizio Iacone & Štěpána Lazarová, 2019. "Semiparametric Detection of Changes in Long Range Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(5), pages 693-706, September.
    • Handle: RePEc:bla:jtsera:v:40:y:2019:i:5:p:693-706
    DOI: 10.1111/jtsa.12448
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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

    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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