Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models

Sylvia Frühwirth-Schnatter () and Christoph Pamminger ()
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Sylvia Frühwirth-Schnatter: Department of Applied Statistics, Johannes Kepler University Linz, Austria, http://www.ifas.jku.at/e2571/e2626/index_ger.html
Christoph Pamminger: Department of Applied Statistics, Johannes Kepler University Linz, Austria, http://www.ifas.jku.at/e2571/e2698/index_ger.html

No 2009-07, NRN working papers from The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria

Abstract: Two approaches for model-based clustering of categorical time series based on time- homogeneous first-order Markov chains are discussed. For Markov chain clustering the in- dividual transition probabilities are fixed to a group-specific transition matrix. In a new approach called Dirichlet multinomial clustering the rows of the individual transition matri- ces deviate from the group mean and follow a Dirichlet distribution with unknown group- specific hyperparameters. Estimation is carried out through Markov chain Monte Carlo. Various well-known clustering criteria are applied to select the number of groups. An appli- cation to a panel of Austrian wage mobility data leads to an interesting segmentation of the Austrian labor market.

Keywords: Markov chain Monte Carlo; model-based clustering; panel data; transition matrices; labor market; wage mobility (search for similar items in EconPapers)
Pages: 42 pages
Date: 2009-07
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:jku:nrnwps:2009_07

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