Computation of Theoretical Autocovariance Matrices of Multivariate Autoregressive Moving Average Time Series
推导了多元ARMA过程理论自协方差与参数的矩阵表达式，并设计了一种比现有方法更高效的计算程序，尤其能减少精确最大似然估计的计算负担。
SUMMARY Matrix expressions relating the theoretical autocovariances of autoregressive moving average (ARMA) processes to their parameters are derived and used to design an efficient procedure for computing autocovariance sequences of multivariate ARMA processes. The method proposed is more efficient than others suggested in the literature and, in particular, reduces the computational burden associated with exact maximum likelihood estimation of ARMA models. The closed form expressions facilitate the implementation of algorithms for computing multivariate autocovariances.