Spectral Subsampling MCMC for Stationary Multivariate Time Series with Applications to Vector ARTFIMA Processes

Villani, M; Quiroz, M; Kohn, R; Salomone, R

Spectral Subsampling MCMC for Stationary Multivariate Time Series with Applications to Vector ARTFIMA Processes

Villani, M Quiroz, M

Kohn, R Salomone, R

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Econometrics and Statistics, 2024, 32, pp. 98-121
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Villani, M
dc.contributor.author	Quiroz, M https://orcid.org/0000-0002-4400-9184
dc.contributor.author	Kohn, R
dc.contributor.author	Salomone, R
dc.date.accessioned	2025-01-29T06:35:27Z
dc.date.available	2025-01-29T06:35:27Z
dc.date.issued	2024
dc.identifier.citation	Econometrics and Statistics, 2024, 32, pp. 98-121
dc.identifier.issn	2452-3062
dc.identifier.issn	2452-3062
dc.identifier.uri	http://hdl.handle.net/10453/184586
dc.description.abstract	A multivariate generalisation of the Whittle likelihood is used to extend spectral subsampling MCMC to stationary multivariate time series by subsampling matrix-valued periodogram observations in the frequency domain. To assess the performance of the methodology in challenging problems, a multivariate generalisation of the autoregressive tempered fractionally integrated moving average model (ARTFIMA) is introduced and some of its properties derived. Bayesian inference based on the Whittle likelihood is demonstrated to be a fast and accurate alternative to the exact time domain likelihood. Spectral subsampling is shown to provide up to two orders of magnitude additional speed-up, while retaining MCMC sampling efficiency and accuracy, compared to spectral methods using the full dataset.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Econometrics and Statistics
dc.relation.isbasedon	10.1016/j.ecosta.2022.10.001
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	3802 Econometrics
dc.subject.classification	4905 Statistics
dc.title	Spectral Subsampling MCMC for Stationary Multivariate Time Series with Applications to Vector ARTFIMA Processes
dc.type	Journal Article
utslib.citation.volume	32
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-01-29T06:35:25Z
pubs.publication-status	Published
pubs.volume	32

Abstract:

A multivariate generalisation of the Whittle likelihood is used to extend spectral subsampling MCMC to stationary multivariate time series by subsampling matrix-valued periodogram observations in the frequency domain. To assess the performance of the methodology in challenging problems, a multivariate generalisation of the autoregressive tempered fractionally integrated moving average model (ARTFIMA) is introduced and some of its properties derived. Bayesian inference based on the Whittle likelihood is demonstrated to be a fast and accurate alternative to the exact time domain likelihood. Spectral subsampling is shown to provide up to two orders of magnitude additional speed-up, while retaining MCMC sampling efficiency and accuracy, compared to spectral methods using the full dataset.

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http://hdl.handle.net/10453/184586