DP4650 Model-based Clustering of Multiple Time Series

Author(s): Sylvia Frühwirth-Schnatter, Sylvia Kaufmann
Publication Date: September 2004
Keyword(s): clustering, Markov chain Monte Carlo, Markov Switching, mixture modelling, panel data
JEL(s): C11, C33, E32
Programme Areas: International Macroeconomics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=4650

We propose to use the attractiveness of pooling relatively short time series that display similar dynamics, but without restricting to pooling all into one group. We suggest estimating the appropriate grouping of time series simultaneously along with the group-specific model parameters. We cast estimation into the Bayesian framework and use Markov chain Monte Carlo simulation methods. We discuss model identification and base model selection on marginal likelihoods. A simulation study documents the efficiency gains in estimation and forecasting that are realized when appropriately grouping the time series of a panel. Two economic applications illustrate the usefulness of the method in analysing also extensions to Markov switching within clusters and heterogeneity within clusters, respectively.