Model-Based Clustering of Multiple Time Series
提出用有限混合模型将多个时间序列分成若干组，每组内用相同计量模型，通过贝叶斯MCMC同时估计分组和参数，模拟显示比整体合并更高效，并扩展到未观测异质性和马尔可夫切换。
We propose to pool multiple time series into several groups using finite-mixture models. Within each group, the same econometric model holds. We estimate the groups of time series simultaneously with the group-specific model parameters using Bayesian Markov chain Monte Carlo simulation methods. We discuss model identification and base model selection on marginal likelihoods. With a simulation study, we document the efficiency gains in estimation and forecasting realized relative to overall pooling of the time series. To illustrate the usefulness of the method, we analyze extensions to unobserved heterogeneity and to Markov switching within clusters.