Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Sophie Achard, Irène Gannaz
Title: Wavelet-Based and Fourier-Based Multivariate Whittle Estimation: multiwave
Abstract: Multivariate time series with long-dependence are observed in many applications such as finance, geophysics or neuroscience. Many packages provide estimation tools for univariate settings but few are addressing the problem of long-dependence estimation for multivariate settings. The package multiwave is providing efficient estimation procedures for multivariate time series. Two semi-parametric estimation methods of the long-memory exponents and long-run covariance matrix of time series are implemented. The first one is the Fourier-based estimation proposed by Shimotsu (2007) and the second one is a wavelet-based estimation described in Achard and Gannaz (2016). The objective of this paper is to provide an overview of the R package multiwave with its practical application perspectives.

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Paper: Wavelet-Based and Fourier-Based Multivariate Whittle Estimation: multiwave     Download PDF (Downloads: 857)
multiwave_1.4.tar.gz: R source package Download (Downloads: 55; 912KB) Replication materials Download (Downloads: 76; 109KB)

DOI: 10.18637/jss.v089.i06

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.