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Monitoring memory parameter change-points in long-memory time series

Author

Listed:
  • Zhanshou Chen

    (Qinghai Normal University
    Academy of Plateau Science and Sustainability)

  • Yanting Xiao

    (Xi’an University of Technology)

  • Fuxiao Li

    (Xi’an University of Technology)

Abstract

In this paper, we propose two ratio-type statistics to sequentially detect the memory parameter change-points in the long-memory time series. The limiting distributions of monitoring statistics under the no-change-point null hypothesis as well as their consistency under the alternative hypothesis are proved. In particular, a sieve bootstrap approximation method is proposed to determine the critical values. Extensive simulations indicate that the new monitoring procedures perform well in finite samples. Finally, we illustrate our monitoring procedures by two sets of real data.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:empeco:v:60:y:2021:i:5:d:10.1007_s00181-020-01840-4
    DOI: 10.1007/s00181-020-01840-4
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    More about this item

    Keywords

    Long-memory process; Change-point monitoring; Sieve bootstrap; Fractional Brownian motion;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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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