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

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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Paper provided by The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria in its series NRN working papers with number 2009-07.

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Length: 42 pages
Date of creation: Jul 2009
Date of revision:
Handle: RePEc:jku:nrnwps:2009_07
Contact details of provider: Postal: NRN Labor Economics and the Welfare State, c/o Rudolf Winter-Ebmer, Altenbergerstr. 69, 4040 Linz
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  1. Andrea Weber, 2002. "State dependence and wage dynamics: a heterogeneous Markov chain model for wage mobility in Austria," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 D2-2, International Conferences on Panel Data.
  2. Thomas Raferzeder & Rudolf Winter-Ebmer, 2004. "Who is on the rise in Austria: Wage mobility and mobility risk," Economics working papers 2004-11, Department of Economics, Johannes Kepler University Linz, Austria.
  3. 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.
  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. Peter Lenk & Wayne DeSarbo, 2000. "Bayesian inference for finite mixtures of generalized linear models with random effects," Psychometrika, Springer, vol. 65(1), pages 93-119, March.
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