The exact likelihood of an autoregressive-moving average model with incomplete data

Abstract

A transformation is developed which allows computationally efficient evaluation of the exact likelihood of an autoregressive-moving average model with incomplete data. Timings are presented which indicate that the new method is preferable to the Kalman filter in many instances. Extensions to nonstationary and vector models are discussed.