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Sieve bootstrap monitoring for change from short to long memory

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  • Chen, Zhanshou
  • Xing, Yuhong
  • Li, Fuxiao

Abstract

This paper proposes a variance ratio statistic to monitor changes from short to long memory processes. The asymptotic distribution is derived under the null hypothesis and the consistency of the monitoring procedure is proven under the alternative hypothesis. A sieve bootstrap approximation method is introduced to determine the critical values. Simulations indicate that the new procedure is quite robust for many types of innovation processes and performs better than the existing retrospective test.

Suggested Citation

  • Chen, Zhanshou & Xing, Yuhong & Li, Fuxiao, 2016. "Sieve bootstrap monitoring for change from short to long memory," Economics Letters, Elsevier, vol. 140(C), pages 53-56.
    • Handle: RePEc:eee:ecolet:v:140:y:2016:i:c:p:53-56
    DOI: 10.1016/j.econlet.2015.12.023
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    References listed on IDEAS

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    1. Philipp Sibbertsen & Robinson Kruse, 2009. "Testing for a break in persistence under long‐range dependencies," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(3), pages 263-285, May.
    2. Philipp Sibbertsen & Juliane Willert, 2012. "Testing for a break in persistence under long-range dependencies and mean shifts," Statistical Papers, Springer, vol. 53(2), pages 357-370, May.
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    11. Chen, Zhanshou & Jin, Zi & Tian, Zheng & Qi, Peiyan, 2012. "Bootstrap testing multiple changes in persistence for a heavy-tailed sequence," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2303-2316.
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