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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- 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.
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- 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.
- 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.
- 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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