Efficient Estimation of Multivariate Moving Average Autocovariances
提出一种估计d维q阶移动平均过程自协方差的方法，其渐近协方差矩阵与高斯极大似然估计相同，通过将周期图对自协方差进行广义最小二乘回归得到，并扩展到最小二乘预测和蒙特卡洛模拟。
This paper proposes a method for estimating the autocovariances of a d -dimensional moving average process of order q . The estimators have the same asymptotic covariance matrix as those obtained by maximizing a Gaussian likelihood, and are obtained by performing a generalized least squares regression of the periodogram on the autocovariances, thus extending Parzen's (1971) estimators for d = 1. An application to least squares prediction is described and the results of a Monte Carlo simulation are presented.