Some Results on Multivariate Autoregressive Index Models
研究了如何用少量指数序列来概括多元自回归时间序列中的历史信息，用于预测，并讨论了最大似然估计和估计量的渐近性质，附有数值例子。
We discuss methods for modelling multivariate autoregressive time series in terms of a smaller number of index series which are chosen to provide as complete a summary as possible of the past information contained in the original series necessary for prediction purposes. The maximum likelihood method of estimation and asymptotic properties of estimators of the coefficients which determine the index variables, a well as the corresponding autoregressive coefficients, are discussed. A numerical example is presented to illustrate the use of the autoregressive index models.