Autoregressive moving average model for matrix time series

Shujin Wu and Ping Bi

Statistical Theory and Related Fields, 2023, vol. 7, issue 4, 318-335

Abstract: In the paper, the autoregressive moving average model for matrix time series (MARMA) is investigated. The properties of the MARMA model are investigated by using the conditional least square estimation, the conditional maximum likelihood estimation, the projection theorem in Hilbert space and the decomposition technique of time series, which include necessary and sufficient conditions for stationarity and invertibility, model parameter estimation, model testing and model forecasting.

Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/24754269.2023.2262360 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tstfxx:v:7:y:2023:i:4:p:318-335

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tstf20

DOI: 10.1080/24754269.2023.2262360

Access Statistics for this article

Statistical Theory and Related Fields is currently edited by Zhao Wei

More articles in Statistical Theory and Related Fields from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().