Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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.

Page views:: 298. Submitted: 2016-11-25. Published: 2019-05-13.
Paper: Wavelet-Based and Fourier-Based Multivariate Whittle Estimation: multiwave     Download PDF (Downloads: 79)
Supplements:
multiwave_1.4.tar.gz: R source package Download (Downloads: 9; 912KB)
v89i06-replication.zip: Replication materials Download (Downloads: 9; 109KB)

DOI: 10.18637/jss.v089.i06

by
This work is licensed under the licenses
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.