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Article: Autoregressive Models for Matrix-valued Time Series

TitleAutoregressive Models for Matrix-valued Time Series
Authors
KeywordsAutoregressive
Bilinear
Economic indicators
Kronecker product
Multivariate time series
Issue Date2020
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jeconom
Citation
Journal of Econometrics, 2020, Epub 2020-08-22 How to Cite?
AbstractIn finance, economics and many other fields, observations in a matrix form are often generated over time. For example, a set of key economic indicators are regularly reported in different countries every quarter. The observations at each quarter neatly form a matrix and are observed over consecutive quarters. Dynamic transport networks with observations generated on the edges can be formed as a matrix observed over time. Although it is natural to turn the matrix observations into long vectors, then use the standard vector time series 2 models for analysis, it is often the case that the columns and rows of the matrix represent different types of structures that are closely interplayed. In this paper we follow the autoregression for modeling time series and propose a novel matrix autoregressive model in a bilinear form that maintains and utilizes the matrix structure to achieve a substantial dimensional reduction, as well as more interpretability. Probabilistic properties of the models are investigated. Estimation procedures with their theoretical properties are presented and demonstrated with simulated and real examples.
Persistent Identifierhttp://hdl.handle.net/10722/286061
ISSN
2020 Impact Factor: 2.388
2015 SCImago Journal Rankings: 3.781
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChen, R-
dc.contributor.authorXiao, H-
dc.contributor.authorYang, D-
dc.date.accessioned2020-08-31T06:58:32Z-
dc.date.available2020-08-31T06:58:32Z-
dc.date.issued2020-
dc.identifier.citationJournal of Econometrics, 2020, Epub 2020-08-22-
dc.identifier.issn0304-4076-
dc.identifier.urihttp://hdl.handle.net/10722/286061-
dc.description.abstractIn finance, economics and many other fields, observations in a matrix form are often generated over time. For example, a set of key economic indicators are regularly reported in different countries every quarter. The observations at each quarter neatly form a matrix and are observed over consecutive quarters. Dynamic transport networks with observations generated on the edges can be formed as a matrix observed over time. Although it is natural to turn the matrix observations into long vectors, then use the standard vector time series 2 models for analysis, it is often the case that the columns and rows of the matrix represent different types of structures that are closely interplayed. In this paper we follow the autoregression for modeling time series and propose a novel matrix autoregressive model in a bilinear form that maintains and utilizes the matrix structure to achieve a substantial dimensional reduction, as well as more interpretability. Probabilistic properties of the models are investigated. Estimation procedures with their theoretical properties are presented and demonstrated with simulated and real examples.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jeconom-
dc.relation.ispartofJournal of Econometrics-
dc.subjectAutoregressive-
dc.subjectBilinear-
dc.subjectEconomic indicators-
dc.subjectKronecker product-
dc.subjectMultivariate time series-
dc.titleAutoregressive Models for Matrix-valued Time Series-
dc.typeArticle-
dc.identifier.emailYang, D: dyanghku@hku.hk-
dc.identifier.authorityYang, D=rp02487-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jeconom.2020.07.015-
dc.identifier.scopuseid_2-s2.0-85089754231-
dc.identifier.hkuros313557-
dc.identifier.volumeEpub 2020-08-22-
dc.identifier.isiWOS:000632248500015-
dc.publisher.placeNetherlands-
dc.identifier.issnl0304-4076-

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