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

JOURNAL OF MULTIVARIATE ANALYSIS

卷:115

Article

Kim, Young Min^1,2  Nordman, Daniel J.¹ 

[1] Iowa State Univ, Dept Stat, Ames, IA 50010 USA

[2] SUNY Buffalo, Dept Biostat, Buffalo, NY 14214 USA

关键词: FARIMA;    Interval estimation;    Long memory;    Spectral density;    Periodogram;   

DOI  :  10.1016/j.jmva.2012.10.018

来源: Elsevier

【 摘 要 】

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. (C) 2012 Elsevier Inc. All rights reserved.

【 授权许可】

Free