Maximum Likelihood Estimation of Multivariate Autoregressive-Moving Average Models.

摘要

Algorithms for computing the exact likelihood function of n successive observation vectors from an s-variate autoregressive moving average process of order (p,q) are developed. A quasi-Newton method is used to maximize the likelihood function with respect to the parameters of the process. Monte Carlo simulations are performed to compare the parameter estimates obtained by maximizing the exact likelihood function versus those obtained by maximizing various approximate forms of the likelihood function. (Author)