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June 2010 Model-based clustering of categorical time series
Sylvia Frühwirth-Schnatter, Christoph Pamminger
Bayesian Anal. 5(2): 345-368 (June 2010). DOI: 10.1214/10-BA606

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 individual transition probabilities are fixed to a group-specific transition matrix. In a new approach called Dirichlet multinomial clustering the rows of the individual transition matrices 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 application to a panel of Austrian wage mobility data leads to an interesting segmentation of the Austrian labor market.

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Sylvia Frühwirth-Schnatter. Christoph Pamminger. "Model-based clustering of categorical time series." Bayesian Anal. 5 (2) 345 - 368, June 2010. https://doi.org/10.1214/10-BA606

Information

Published: June 2010
First available in Project Euclid: 20 June 2012

zbMATH: 1330.62256
MathSciNet: MR2719656
Digital Object Identifier: 10.1214/10-BA606

Keywords: labor market , Markov chain Monte Carlo , Model-based clustering , panel data , transition matrices , wage mobility

Rights: Copyright © 2010 International Society for Bayesian Analysis

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Vol.5 • No. 2 • June 2010
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