A Fast Estimation Method for the Vector Autoregressive Moving Average Model With Exogenous Variables
提出一种快速简单的算法，用于估计大型多元时间序列和分布滞后模型的参数，估计量渐近正态无偏，方差随样本量增大而减小，特别适用于大型模型，速度远快于最大化算法。
Abstract A very fast and simple algorithm for estimation of the parameters of large multivariate time series and distributed lag models is presented. An analysis of the distribution of the estimates shows that they are asymptotically normal and unbiased, and that they have a variance that decreases like 1/n, n being the sample size. The algorithm is especially applicable for estimation of large multivariate models where it is generally many times faster than maximalization algorithms.