Analyzing Permanent and Transient Influences in Multiple Time Series Models
开发了多种多时间序列模型，将时间序列分解为永久和暂时成分，用于分析预测M1和M2货币供应量，该分解能提高预测精度，并估计方差协方差分配参数。
Several multiple time series models are developed and applied to the analysis and forecasting of the M1 and M2 money supply aggregates. These models feature a decomposition of the time series into permanent and transient influences or components. This decomposition appears to enhance forecasting accuracy and is associated with a variance-covariance allocation parameter that is also estimated from the data. Conditional maximum likelihood estimates for model parameters are presented as well as a numerical algorithm that is an adaptation of Marquardt's algorithm.