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Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering
Author(s) -
FrühwirthSchnatter Sylvia,
Pamminger Christoph,
Weber Andrea,
WinterEbmer Rudolf
Publication year - 2011
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.1249
Subject(s) - cluster analysis , markov chain , econometrics , multinomial logistic regression , categorical variable , statistical inference , bayesian probability , multinomial probit , inference , statistical model , markov model , computer science , economics , mathematics , statistics , artificial intelligence , machine learning , probit model
SUMMARY 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 dataset. 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 difficulty in extending distance‐based clustering approaches to the problem of identifying groups of similar time series in a panel of discrete‐valued time series. We use Markov chain clustering, 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. Copyright © 2011 John Wiley & Sons, Ltd.