Model-Based Clustering of Multiple Time Series
多条时间序列的模型聚类
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…