A simple noniterative estimator for moving average models

We examine a simple noniterative estimator for the parameters of a general moving average process. This non-maximum-likelihood estimator derives moving average model parameters directly from the coefficients of an approximating autoregressive model. The estimator is evaluated through asymptotic expansions and by simulation, and is also compared with maximum likelihood and the related estimator of Durbin (1959). The comparison with maximum likelihood by simulation suggests a variety of circumstances in which the simpler estimator may be appropriate despite the advantage of maximum likelihood for properly-specified low-order models.