Estimators of Long-Memory: Fourier versus Wavelets
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Estimators of Long-Memory: Fourier versus Wavelets
Author(s) :
Fay, Gilles [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Moulines, Éric [Auteur]
Laboratoire Traitement et Communication de l'Information [LTCI]
Roueff, François [Auteur]
Laboratoire Traitement et Communication de l'Information [LTCI]
Taqqu, Murad [Auteur]
Boston University [Boston] [BU]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Moulines, Éric [Auteur]
Laboratoire Traitement et Communication de l'Information [LTCI]
Roueff, François [Auteur]
Laboratoire Traitement et Communication de l'Information [LTCI]
Taqqu, Murad [Auteur]
Boston University [Boston] [BU]
Journal title :
Econometrics
Pages :
159-177
Publisher :
MDPI
Publication date :
2009-08-01
ISSN :
2225-1146
English keyword(s) :
Wavelet analysis
long range dependence
semi-parametric estimation
long range dependence
semi-parametric estimation
HAL domain(s) :
Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]
Statistiques [stat]/Théorie [stat.TH]
English abstract : [en]
There have been a number of papers written on semi-parametric estimation methods of the long-memory exponent of a time series, some applied, others theoretical. Some using Fourier methods, others using a wavelet-based ...
Show more >There have been a number of papers written on semi-parametric estimation methods of the long-memory exponent of a time series, some applied, others theoretical. Some using Fourier methods, others using a wavelet-based technique. In this paper, we compare the Fourier and wavelet approaches to the local regression method and to the local Whittle method. We provide an overview of these methods, describe what has been done, indicate the available results and the conditions under which they hold. We discuss their relative strengths and weaknesses both from a practical and a theoretical perspective. We also include a simulation-based comparison. The software written to support this work is available on demand and we illustrate its use at the end of the paper.Show less >
Show more >There have been a number of papers written on semi-parametric estimation methods of the long-memory exponent of a time series, some applied, others theoretical. Some using Fourier methods, others using a wavelet-based technique. In this paper, we compare the Fourier and wavelet approaches to the local regression method and to the local Whittle method. We provide an overview of these methods, describe what has been done, indicate the available results and the conditions under which they hold. We discuss their relative strengths and weaknesses both from a practical and a theoretical perspective. We also include a simulation-based comparison. The software written to support this work is available on demand and we illustrate its use at the end of the paper.Show less >
Language :
Anglais
Popular science :
Non
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