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A frequency domain bootstrap for Whittle estimation under long-range dependence

Listed author(s):
  • Kim, Young Min
  • Nordman, Daniel J.
Registered author(s):

    Whittle estimation is a common technique for fitting parametric spectral density functions to time series, in an effort to model the underlying covariance structure. However, Whittle estimators from long-range dependent processes can exhibit slow convergence to their Gaussian limit law so that calibrating confidence intervals with normal approximations may perform poorly. As a remedy, we study a frequency domain bootstrap (FDB) for approximating the distribution of Whittle estimators. The method provides valid distribution estimation for a broad class of stationary, long-range (or short-range) dependent linear processes, without stringent assumptions on the distribution of the underlying process. A large simulation study shows that the FDB approximations often improve normal approximations for setting confidence intervals for Whittle parameters in spectral models with strong dependence.

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    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 115 (2013)
    Issue (Month): C ()
    Pages: 405-420

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    Handle: RePEc:eee:jmvana:v:115:y:2013:i:c:p:405-420
    DOI: 10.1016/j.jmva.2012.10.018
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    1. Jentsch, Carsten & Kreiss, Jens-Peter, 2010. "The multiple hybrid bootstrap -- Resampling multivariate linear processes," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2320-2345, November.
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    7. Nordman, Daniel J. & Lahiri, Soumendra N., 2005. "Validity Of The Sampling Window Method For Long-Range Dependent Linear Processes," Econometric Theory, Cambridge University Press, vol. 21(06), pages 1087-1111, December.
    8. Donald W. K. Andrews & Yixiao Sun, 2004. "Adaptive Local Polynomial Whittle Estimation of Long-range Dependence," Econometrica, Econometric Society, vol. 72(2), pages 569-614, March.
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