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Detection Of Nonconstant Long Memory Parameter

Author

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  • Lavancier, Frédéric
  • Leipus, Remigijus
  • Philippe, Anne
  • Surgailis, Donatas

Abstract

This article deals with detection of a nonconstant long memory parameter in time series. The null hypothesis presumes stationary or nonstationary time series with a constant long memory parameter, typically an I (d) series with d > −.5 . The alternative corresponds to an increase in persistence and includes in particular an abrupt or gradual change from I (d1) to I (d2), −.5

Suggested Citation

  • Lavancier, Frédéric & Leipus, Remigijus & Philippe, Anne & Surgailis, Donatas, 2013. "Detection Of Nonconstant Long Memory Parameter," Econometric Theory, Cambridge University Press, vol. 29(5), pages 1009-1056, October.
    • Handle: RePEc:cup:etheor:v:29:y:2013:i:05:p:1009-1056_00
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    Cited by:

    1. M. A. Limam & V. Terraza & M. Terraza, 2017. "Hedge Fund Return Dynamics: Long Memory and Regime Switching," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 8(4), pages 148-166, October.
    2. 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.
    3. Bibinger, Markus, 2020. "Cusum tests for changes in the Hurst exponent and volatility of fractional Brownian motion," Statistics & Probability Letters, Elsevier, vol. 161(C).
    4. Sabzikar, Farzad & Surgailis, Donatas, 2018. "Invariance principles for tempered fractionally integrated processes," Stochastic Processes and their Applications, Elsevier, vol. 128(10), pages 3419-3438.
    5. Farzad Sabzikar & Qiying Wang & Peter C.B. Phillips, 2018. "Asymptotic Theory for Near Integrated Process Driven by Tempered Linear Process," Cowles Foundation Discussion Papers 2131, Cowles Foundation for Research in Economics, Yale University.

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