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

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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.

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  • Sylvia Frühwirth-Schnatter & Christoph Pamminger, 2009. "Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models," NRN working papers 2009-07, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
    • Handle: RePEc:jku:nrnwps:2009_07
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    1. Fruhwirth-Schnatter, Sylvia & Fruhwirth, Rudolf, 2007. "Auxiliary mixture sampling with applications to logistic models," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3509-3528, April.
    2. Weber, Andrea, 2002. "State Dependence and Wage Dynamics: A Heterogeneous Markov Chain Model for Wage Mobility in Austria," Economics Series 114, Institute for Advanced Studies.
    3. Fabio Canova, 2004. "Testing for Convergence Clubs in Income Per Capita: A Predictive Density Approach," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(1), pages 49-77, February.
    4. Frydman, Halina, 2005. "Estimation in the Mixture of Markov Chains Moving With Different Speeds," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1046-1053, September.
    5. Thomas Raferzeder & Rudolf Winter-Ebmer, 2007. "Who is on the rise in Austria: Wage mobility and mobility risk," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 5(1), pages 39-51, April.
    6. Fraley C. & Raftery A.E., 2002. "Model-Based Clustering, Discriminant Analysis, and Density Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 611-631, June.
    7. Peter Lenk & Wayne DeSarbo, 2000. "Bayesian inference for finite mixtures of generalized linear models with random effects," Psychometrika, Springer;The Psychometric Society, vol. 65(1), pages 93-119, March.
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    Cited by:

    1. Aßmann, Christian & Boysen-Hogrefe, Jens, 2011. "A Bayesian approach to model-based clustering for binary panel probit models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 261-279, January.

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