Abstract (may include machine translation)
A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.
Original language | English |
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Pages (from-to) | 1897-1900 |
Number of pages | 4 |
Journal | AIP Conference Proceedings |
Volume | 1281 |
DOIs | |
State | Published - 2010 |
Externally published | Yes |
Event | International Conference on Numerical Analysis and Applied Mathematics 2010, ICNAAM-2010 - Rhodes, Greece Duration: 19 Sep 2010 → 25 Sep 2010 |
Keywords
- Auxiliary Mixture Sampler
- Bayesian Statistics
- Classification
- Markov Chain Monte Carlo
- Multinomial Logit Model
- Panel Data
- Random Utility Model
- Transition Matrices