Recursive identification for multivariate autoregressive equation-error systems with autoregressive noise

This paper considers the recursive identification problems for a class of multivariate autoregressive equation-error systems with autoregressive noise. By decomposing the system into several regressive identification subsystems, a maximum likelihood recursive generalised least squares identification algorithm is proposed to identify the parameter vectors in each subsystem. In addition, a multivariate recursive generalised least squares algorithm is derived as a comparison. The numerical simulation results indicate that the maximum likelihood recursive generalised least squares algorithm can effectively estimate the parameters of the multivariate autoregressive equation-error autoregressive systems and get more accurate parameter estimates than the multivariate recursive generalised least squares algorithm.