An algorithm for the exact likelihood of a stationary vector autoregressive‐moving average model

José Alberto Mauricio

Journal of Time Series Analysis, 2002, vol. 23, issue 4, 473-486

Abstract: The so‐called innovations form of the likelihood function implied by a stationary vector autoregressive‐moving average model is considered without directly using a state–space representation. Specifically, it is shown in detail how to compute the exact likelihood by an adaptation to the multivariate case of the innovations algorithm of Ansley (1979) for univariate models. Comparisons with other existing methods are also provided, showing that the algorithm described here is computationally more efficient than the fastest methods currently available in many cases of practical interest.

Date: 2002
References: Add references at CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
https://doi.org/10.1111/1467-9892.00273

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:bla:jtsera:v:23:y:2002:i:4:p:473-486

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782

Access Statistics for this article

Journal of Time Series Analysis is currently edited by M.B. Priestley

More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().