Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment*
提出一类含周期未观测成分的季节性时间序列模型，应用于美国月度失业率数据，发现显著的周期成分，并比较了周期与非周期模型的性能。
Abstract We introduce a general class of periodic unobserved component (UC) time series models with stochastic trend and seasonal components and with a novel periodic stochastic cycle component. The general state space formulation of the periodic model allows for exact maximum likelihood estimation, signal extraction and forecasting. The consequences for model‐based seasonal adjustment are discussed. The new periodic model is applied to postwar monthly US unemployment series from which we identify a significant periodic stochastic cycle. A detailed periodic analysis is presented including a comparison between the performances of periodic and non‐periodic UC models.