Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/115050 
Erscheinungsjahr: 
2010
Schriftenreihe/Nr.: 
NRN Working Paper, NRN: The Austrian Center for Labor Economics and the Analysis of the Welfare State No. 1014
Verlag: 
Johannes Kepler University Linz, NRN - The Austrian Center for Labor Economics and the Analysis of the Welfare State, Linz
Zusammenfassung: 
This paper analyzes patterns in the earnings development of young labor market entrants over their life cycle. We identify four distinctly different types of transition patterns between discrete earnings states in a large administrative data set. Further, we investigate the effects of labor market conditions at the time of entry on the probability of belonging to each transition type. To estimate our statistical model we use a model-based clustering approach. The statistical challenge in our application comes from the di±culty in extending distance-based clustering approaches to the problem of identify groups of similar time series in a panel of discrete-valued time series. We use Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter (2010), which is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to analyze group membership we present an extension to this approach by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule using a multinomial logit model.
Schlagwörter: 
Labor Market Entry Conditions
Transition Data
Markov Chain Monte Carlo
Multinomial Logit
Panel Data
Auxiliary Mixture Sampler
Bayesian Statistics
Dokumentart: 
Working Paper

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