Estimators of Long-Memory: Fourier versus Wavelets

G. Faÿ 1 E. Moulines F. Roueff M.S. Taqqu
1 APC-Gravitation - APC - Gravitation
APC - UMR 7164 - AstroParticule et Cosmologie, Max-Planck-Institut für Gravitationsphysik (Albert-Einstein-Institut)
Abstract : 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 describe its use in the appendix.
Type de document :
Article dans une revue
Journal of Econometrics, Elsevier, 2009, 151, pp.159-177. 〈10.1016/j.jeconom.2009.03.005〉
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Soumis le : mardi 6 novembre 2007 - 12:39:01
Dernière modification le : mercredi 31 janvier 2018 - 12:14:37




G. Faÿ, E. Moulines, F. Roueff, M.S. Taqqu. Estimators of Long-Memory: Fourier versus Wavelets. Journal of Econometrics, Elsevier, 2009, 151, pp.159-177. 〈10.1016/j.jeconom.2009.03.005〉. 〈in2p3-00185534〉



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