Journal of Time Series Analysis

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

Journal Article

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.

Related Topics

Related Publications

Related Content

Site Footer


This website is provided by John Wiley & Sons Limited, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (Company No: 00641132, VAT No: 376766987)

Published features on are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.