Nonlinear maximum likelihood estimation of AR time series

Describes an algorithm for finding the exact maximum likelihood (ML) estimators for the parameters of an autoregressive time series. The authors demonstrate that the ML normal equations can be written as an interdependent set of cubic and quadratic equations in the AR polynomial coefficients. They present an algorithm, based on the properties of Sylvester resolvent matrices, that solves this set of non-linear equations for low-order problems.<>