Canonical Correlation in Multivariate Time Series Analysis With an Application to One-Year-Ahead and Multiyear-Ahead Macroeconomic Forecasting
提出一种基于典型相关技术的多元时间序列直接预测方法，无需先估计滞后阶数和参数，通过协方差矩阵的奇异值分解得到最优预测，并用于预测14个国家1974-1984年的年度及多年经济增长率。
Abstract A simple one-period-ahead and multiperiod-ahead prediction procedure for multivariate time series is suggested, based on the canonical correlation technique. The prediction procedure is direct in the sense that no lag orders and parameters have to be estimated first, as in the usual ARMAX or VAR parameterizations of multivariate stationary stochastic processes. A best (in the mean squared error sense) predictor can be obtained directly using singular-value decompositions of covariance matrices. The procedure is used to forecast one-year-ahead and multiyear-ahead national growth rates of 14 countries for the years 1974–1984.