Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/115016
Authors: 
Frühwirth-Schnatter, Sylvia
Pamminger, Christoph
Year of Publication: 
2009
Series/Report no.: 
NRN Working Paper, NRN: The Austrian Center for Labor Economics and the Analysis of the Welfare State 0907
Abstract: 
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.
Subjects: 
Markov chain Monte Carlo
model-based clustering
panel data
transition matrices
labor market
wage mobility
Document Type: 
Working Paper

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