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Monitoring change in persistence in linear time series

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  • Chen, Zhanshou
  • Tian, Zheng
  • Wei, Yuesong

Abstract

A moving ratio monitoring scheme is proposed to detect changes between trend stationary (I(0)) and difference stationary (I(1)) regimes. It is consistent both for I(1) to I(0) and I(0) to I(1) change, and has less computation time. The empirical size, power and average run length of the test are evaluated in a simulation study. Simulations indicate that the new method achieves a far superior finite sample performance as compared with the existing variance ratio monitoring procedure in the literature. A modified version is also considered under I(0) null hypothesis in the presence of a variance shift. In addition, we apply the procedure to investigate the US inflation rate data and show how it is used to detect multiple changes in persistence.

Suggested Citation

  • Chen, Zhanshou & Tian, Zheng & Wei, Yuesong, 2010. "Monitoring change in persistence in linear time series," Statistics & Probability Letters, Elsevier, vol. 80(19-20), pages 1520-1527, October.
    • Handle: RePEc:eee:stapro:v:80:y:2010:i:19-20:p:1520-1527
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    References listed on IDEAS

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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.
    2. Skrobotov, Anton, 2015. "Likelihood Ratio Test for Change in Persistence," Published Papers skr001, Russian Presidential Academy of National Economy and Public Administration.
    3. Petrenko, Victoria (Петренко, ВИктория) & Skrobotov, Anton (Скроботов, Антон) & Turuntseva, Maria (Турунцева, Мария), 2016. "Testing of Changes in Persistence and Their Effect on the Forecasting Quality [Тестирование Изменения Инерционности И Влияние На Качество Прогнозов]," Working Papers 542, Russian Presidential Academy of National Economy and Public Administration.
    4. Wagner, Martin & Wied, Dominik, 2014. "Monitoring Stationarity and Cointegration," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100386, Verein für Socialpolitik / German Economic Association.
    5. 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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