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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- Raferzeder, Thomas & Winter-Ebmer, Rudolf, 2004.
"Who is on the Rise in Austria: Wage Mobility and Mobility Risk,"
IZA Discussion Papers
1329, Institute for the Study of Labor (IZA).
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- Andrea Weber, 2002.
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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.
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