Bayesian Analysis of an Unobserved-Component Time Series Model of GDP With Markov-Switching and Time-Varying Growths
提出一个包含马尔可夫转换作为不可观测成分的GDP时间序列模型，该模型具有两个时变漂移成分，分别代表衰退和扩张期的预期增长率。基于美国数据估计显示，扩张期年增长率从1950年的6.4%下降到1990年的3.6%。
We propose an unobserved-component time series model of gross domestic product that includes Markov switching as an unobserved component. In addition to a trend component, the model has two time-varying drift components. One drift represents the expected rate of growth during recession; the other drift represents the expected rate during expansion. Estimates indicate a substantial decline in the latter annual rate for the United States from 6.4% in 1950 to 3.6% by 1990. We have employed weak priors based on prewar data. The estimation makes use of the Gibbs sampler and the Metropolis algorithm.