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High-Dimensional Time Series Segmentation via Factor-Adjusted Vector Autoregressive Modeling

Cho, Haeran; Maeng, Hyeyoung; Eckley, Idris A.; Fearnhead, Paul

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Authors

Haeran Cho

Idris A. Eckley

Paul Fearnhead



Abstract

Vector autoregressive (VAR) models are popularly adopted for modeling high-dimensional time series, and their piecewise extensions allow for structural changes in the data. In VAR modeling, the number of parameters grow quadratically with the dimensionality which necessitates the sparsity assumption in high dimensions. However, it is debatable whether such an assumption is adequate for handling datasets exhibiting strong serial and cross-sectional correlations. We propose a piecewise stationary time series model that simultaneously allows for strong correlations as well as structural changes, where pervasive serial and cross-sectional correlations are accounted for by a time-varying factor structure, and any remaining idiosyncratic dependence between the variables is handled by a piecewise stationary VAR model. We propose an accompanying two-stage data segmentation methodology which fully addresses the challenges arising from the latency of the component processes. Its consistency in estimating both the total number and the locations of the change points in the latent components, is established under conditions considerably more general than those in the existing literature. We demonstrate the competitive performance of the proposed methodology on simulated datasets and an application to U.S. blue chip stocks data. Supplementary materials for this article are available online.

Citation

Cho, H., Maeng, H., Eckley, I. A., & Fearnhead, P. (2023). High-Dimensional Time Series Segmentation via Factor-Adjusted Vector Autoregressive Modeling. Journal of the American Statistical Association, https://doi.org/10.1080/01621459.2023.2240054

Journal Article Type Article
Acceptance Date Jul 14, 2023
Online Publication Date Jul 26, 2023
Publication Date 2023
Deposit Date Oct 2, 2023
Publicly Available Date Oct 2, 2023
Journal Journal of the American Statistical Association
Print ISSN 0162-1459
Electronic ISSN 1537-274X
Publisher Taylor and Francis Group
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1080/01621459.2023.2240054
Keywords Statistics, Probability and Uncertainty; Statistics and Probability
Public URL https://durham-repository.worktribe.com/output/1755463

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Published Journal Article (Advance Online Version) (2.6 Mb)
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Licence
http://creativecommons.org/licenses/by/4.0/

Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
© 2023 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.





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