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    Rights statement: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Biometrika following peer review. The definitive publisher-authenticated version K Fokianos, M Pitsillou; Testing independence for multivariate time series via the auto-distance correlation matrix, Biometrika, Volume 105, Issue 2, 1 June 2018, Pages 337–352, https://doi.org/10.1093/biomet/asx082

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Testing independence for multivariate time series via the auto-distance correlation matrix

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>1/06/2018
<mark>Journal</mark>Biometrika
Issue number2
Volume105
Number of pages16
Pages (from-to)337-352
Publication statusPublished
Early online date20/01/18
Original languageEnglish

Abstract

We introduce the matrix multivariate auto-distance covariance and correlation functions for time series, discuss their interpretation and develop consistent estimators for practical implementation. We also develop a test of the independent and identically distributed hypothesis for multivariate time series data and show that it performs better than the multivariate Ljung–Box test. We discuss computational aspects and present a data example to illustrate the method.

Bibliographic note

This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Biometrika following peer review. The definitive publisher-authenticated version K Fokianos, M Pitsillou; Testing independence for multivariate time series via the auto-distance correlation matrix, Biometrika, Volume 105, Issue 2, 1 June 2018, Pages 337–352, https://doi.org/10.1093/biomet/asx082