Arbeitspapier
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.
- Language
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Englisch
- Bibliographic citation
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Series: NRN Working Paper, NRN: The Austrian Center for Labor Economics and the Analysis of the Welfare State ; No. 0907
- Classification
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Wirtschaft
- Subject
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Markov chain Monte Carlo
model-based clustering
panel data
transition matrices
labor market
wage mobility
- Event
-
Geistige Schöpfung
- (who)
-
Frühwirth-Schnatter, Sylvia
Pamminger, Christoph
- Event
-
Veröffentlichung
- (who)
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Johannes Kepler University Linz, NRN - The Austrian Center for Labor Economics and the Analysis of the Welfare State
- (where)
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Linz
- (when)
-
2009
- Handle
- Last update
- 10.03.2025, 11:42 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Frühwirth-Schnatter, Sylvia
- Pamminger, Christoph
- Johannes Kepler University Linz, NRN - The Austrian Center for Labor Economics and the Analysis of the Welfare State
Time of origin
- 2009
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