Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models - Sylvia Frühwirth-Schnatter; Christoph Pamminger (Johannes Kepler University Linz, NRN - The Austrian Center for Labor Economics and the Analysis of the Welfare State, 2009)
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.
Keywords: 330; Markov chain Monte Carlo; model-based clustering; panel data; transition matrices; labor market; wage mobility