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.
|Date of creation:||Jul 2009|
|Date of revision:|
|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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114, Institute for Advanced Studies.
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IZA Discussion Papers
1329, Institute for the Study of Labor (IZA).
- 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.
- 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.
- 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.
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