Locally stationary long memory estimation

被引：98

作者：

Roueff, Francois ^{[1
]}

von Sachs, Rainer ^{[2
]}

机构：

[1] CNRS LTCI, Telecom Paris, Inst Telecom, F-75634 Paris 13, France

[2] Catholic Univ Louvain, IMMAQ, Inst Stat Biostat & Sci Actuarielles ISBA, B-1348 Louvain, Belgium

来源：

STOCHASTIC PROCESSES AND THEIR APPLICATIONS | 2011年 / 121卷 / 04期

关键词：

Locally stationary process; Long memory; Semi-parametric estimation; Wavelets; PARAMETER; INFERENCE; MODELS;

D O I：

10.1016/j.spa.2010.12.004

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

There exists a wide literature on parametrically or semi-parametrically modelling strongly dependent time series using a long-memory parameter d, including more recent work on wavelet estimation. As a generalization of these latter approaches, in this work we allow the long-memory parameter d to be varying over time. We adopt a semi-parametric approach in order to avoid fitting a time-varying parametric model, such as tvARFIMA, to the observed data. We study the asymptotic behavior of a local log-regression wavelet estimator of the time-dependent d. Both simulations and a real data example complete our work on providing a fairly general approach. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：813 / 844

页数：32

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