A Simple Noniterative Estimator for Moving Average Models
提出一种从自回归模型系数直接推导移动平均参数的非迭代估计量，通过渐近展开和模拟评估其性能，并与最大似然估计比较，发现简单估计量在某些情况下适用。
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.