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

Young Min Kim and Daniel J. Nordman

Journal of Multivariate Analysis, 2013, vol. 115, issue C, 405-420

Abstract: 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.

Keywords: FARIMA; Interval estimation; Long memory; Spectral density; Periodogram (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:115:y:2013:i:c:p:405-420

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DOI: 10.1016/j.jmva.2012.10.018

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