The exact likelihood function of a vector autoregressive moving average process

Available closed form expressions for the determinant and inverse of the covariance matrix of a set of observations generated by a vector autoregressive moving average model are derived following a unified and simplified approach. Computational guidelines to estimate these models by maximum likelihood or nonlinear least squares methods are also given.