Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models
AbstractTwo 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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Bibliographic InfoPaper 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.
Length: 42 pages
Date of creation: Jul 2009
Date of revision:
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Postal: NRN Labor Economics and the Welfare State, c/o Rudolf Winter-Ebmer, Altenbergerstr. 69, 4040 Linz
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Markov chain Monte Carlo; model-based clustering; panel data; transition matrices; labor market; wage mobility;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-08-02 (All new papers)
- NEP-ECM-2009-08-02 (Econometrics)
- NEP-ETS-2009-08-02 (Econometric Time Series)
- NEP-ORE-2009-08-02 (Operations Research)
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