Trends and Cycles in Macroeconomic Time Series
构建了两个用于年度观测数据的结构时间序列模型，包含趋势、周期和不规则成分，并通过卡尔曼滤波对美国五个宏观经济时间序列进行估计，揭示了序列的动态结构特别是周期行为。
Two structural time series models for annual observations are constructed in terms of trend, cycle, and irregular components. The models are then estimated via the Kalman filter using data on five U.S. macroeconomic time series. The results provide some interesting insights into the dynamic structure of the series, particularly with respect to cyclical behavior. At the same time, they illustrate the development of a model selection strategy for structural time series models.