Model-Based Clustering of Multiple Time Series

32 Pages Posted: 2 Dec 2004

See all articles by Sylvia Fruhwirth-Schnatter

Sylvia Fruhwirth-Schnatter

Johannes Kepler University - Department of Applied Statistics and Econometrics

Sylvia Kaufmann

Oesterreichische Nationalbank - Economic Studies Division; University of Basel - WWZ

Date Written: September 2004

Abstract

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 analyzing also extensions to Markov switching within clusters and heterogeneity within clusters, respectively.

Keywords: Panel data, clustering, mixture modelling, Markov Switching, Markov chain Monte Carlo

JEL Classification: C11, C33, E32

Suggested Citation

Fruhwirth-Schnatter, Sylvia and Kaufmann, Sylvia, Model-Based Clustering of Multiple Time Series (September 2004). CEPR Discussion Paper No. 4650. Available at SSRN: https://ssrn.com/abstract=628774

Sylvia Fruhwirth-Schnatter (Contact Author)

Johannes Kepler University - Department of Applied Statistics and Econometrics ( email )

Altenbergerstrasse 69
Linz
Austria

Sylvia Kaufmann

Oesterreichische Nationalbank - Economic Studies Division ( email )

Otto Wagner-Platz 3
P.O. Box 61
Vienna, 1011
Austria
+43 1 40420-7221 (Phone)

University of Basel - WWZ ( email )

Basel, 4002
Switzerland

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