High-Dimensional Multivariate Time Series With Additional Structure

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dc.citation.firstpage610en_US
dc.citation.issueNumber3en_US
dc.citation.journalTitleJournal of Computational and Graphical Statisticsen_US
dc.citation.lastpage622en_US
dc.citation.volumeNumber26en_US
dc.contributor.authorSchweinberger, Michaelen_US
dc.contributor.authorBabkin, Sergiien_US
dc.contributor.authorEnsor, Katherine B.en_US
dc.contributor.orgCenter for Computational Finance and Economic Systemsen_US
dc.date.accessioned2022-05-25T16:13:35Zen_US
dc.date.available2022-05-25T16:13:35Zen_US
dc.date.issued2017en_US
dc.description.abstractHigh-dimensional multivariate time series are challenging due to the dependent and high-dimensional nature of the data, but in many applications there is additional structure that can be exploited to reduce computing time along with statistical error. We consider high-dimensional vector autoregressive processes with spatial structure, a simple and common form of additional structure. We propose novel high-dimensional methods that take advantage of such structure without making model assumptions about how distance affects dependence. We provide nonasymptotic bounds on the statistical error of parameter estimators in high-dimensional settings and show that the proposed approach reduces the statistical error. An application to air pollution in the USA demonstrates that the estimation approach reduces both computing time and prediction error and gives rise to results that are meaningful from a scientific point of view, in contrast to high-dimensional methods that ignore spatial structure. In practice, these high-dimensional methods can be used to decompose high-dimensional multivariate time series into lower-dimensional multivariate time series that can be studied by other methods in more depth.en_US
dc.identifier.citationSchweinberger, Michael, Babkin, Sergii and Ensor, Katherine B.. "High-Dimensional Multivariate Time Series With Additional Structure." <i>Journal of Computational and Graphical Statistics,</i> 26, no. 3 (2017) Taylor & Francis: 610-622. https://doi.org/10.1080/10618600.2016.1265528.en_US
dc.identifier.doihttps://doi.org/10.1080/10618600.2016.1265528en_US
dc.identifier.urihttps://hdl.handle.net/1911/112403en_US
dc.language.isoengen_US
dc.publisherTaylor & Francisen_US
dc.rightsThis is an author's pre-print. The published article is copyrighted by Taylor & Francis.en_US
dc.titleHigh-Dimensional Multivariate Time Series With Additional Structureen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpre-printen_US
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