A Note on Autoregressive Modeling
研究了向量自回归模型的估计问题，比较了Yule-Walker、最小二乘和Burg型三种估计量的渐近性质，推荐在平稳序列建模中使用Burg型估计量，尤其适用于通过脉冲响应函数分析变量间关系。
This paper addresses the problem of estimating vector autoregressive models. An approach to handling nonstationary (integrated) time series is briefly discussed, but the main emphasis is upon the estimation of autoregressive approximations to stationary processes. Three alternative estimators are considered–the Yule-Walker, least-squares, and Burg-type estimates–and a complete analysis of their asymptotic properties in the stationary case is given. The results obtained, when placed together with those found elsewhere in the literature, lead to the direct recommendation that the less familiar Burg-type estimator should be used in practice when modeling stationary series. This is particularly so when the underlying objective of the analysis is to investigate the interrelationships between variables of interest via impulse response functions and dynamic multipliers.