Model-Based Clustering of Multiple Time Series
Sylvia Frühwirth‐Schnatter, Sylvia Kaufmann · Journal of Business and Economic Statistics · 2007
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.