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Identifier to cite or link to this item: https://hdl.handle.net/10086/22047

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Title
The Wavelet-based Estimation for Long Memory Signal Plus Noise Models
Creator
Creator Name
Nanamiya, Kei Nanamiya, Kei
Affiliation
Affiliation Name
Hitotsubashi University
Editor
Contributor Name
社会科学の高度統計・実証分析拠点構築 = Research Unit for Statistical and Empirical Analysis in Social Sciences
Key Word
wavelet
long memory process
measurement error problem
Abstract
We propose new wavelet-based procedure to estimate the memory parameter of an unobserved process from an observed process affected by noise in order to improve the performance of the estimator by taking into account the dependency of the wavelet coefficients of long memory processes. In our procedure, using the AR (1) approximation for the wavelet transformed long memory processes which is introduced by Craigmile, Guttorp and Percival (2005), we apply the ARMA (1, 1) approximation to the wavelet coefficients of the observed process at each level. We also compare this procedure to the usual wavelet-based procedure by numerical simulations.
Publisher
Institute of Economic Research, Hitotsubashi University
Issued Date
2011-12
Language
English(eng)
Resource Type
technical report
Version Type
VoR
Is Part OfRelation -
Related Title
Global COE Hi-Stat Discussion Paper Series ; No. 210
Relation
Related Identifier
URI
  http://gcoe.ier.hit-u.ac.jp/english/index.html
Number of Pages
35
Sponsorship
グローバルCOEプログラム = Global COE Program
Appears in Collections
Global COE Hi-Stat Discussion Paper Series
URL
https://hdl.handle.net/10086/22047