Work

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

Year: 2022

Type: article

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.

Source: Econometrics and Statistics

DOI: 10.1016/j.ecosta.2022.10.001

Authors: Mattias Villani, Matias Quiroz, Robert Kohn, Robert Salomone

Institutions: Linköping University, Stockholm University, University of Technology Sydney, UNSW Sydney, Queensland University of Technology

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $1,540

Funders: Stiftelsen för Strategisk Forskning, Vetenskapsrådet, Australian Research Council