Bayesian inference for periodic regime-switching models
提出一类非线性时间序列马尔可夫体制转换模型，适用于具有周期特征的季节数据，并采用贝叶斯方法进行估计和推断，以住房开工数据和美国二战后工业生产数据为例。
We present a general class of nonlinear time-series Markov regime-switching models for seasonal data which may exhibit periodic features in the hidden Markov process as well as in the laws of motion in each of the regimes. This class of models allows for non-trivial dependencies between seasonal, cyclical and long-term patterns in the data. To overcome the computational burden we adopt a Bayesian approach to estimation and inference. This paper contains two empirical examples as illustration, one uses housing starts data while the other employs US post-Second World War industrial production. © 1998 John Wiley & Sons, Ltd.