TY - JOUR
T1 - Labor market entry and earnings dynamics
T2 - Bayesian inference using mixtures-of-experts Markov chain clustering
AU - Frühwirth-Schnatter, Sylvia
AU - Pamminger, Christoph
AU - Weber, Andrea
AU - Winter-Ebmer, Rudolf
PY - 2012/11
Y1 - 2012/11
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84870358901&partnerID=8YFLogxK
U2 - 10.1002/jae.1249
DO - 10.1002/jae.1249
M3 - Article
AN - SCOPUS:84870358901
SN - 0883-7252
VL - 27
SP - 1116
EP - 1137
JO - Journal of Applied Econometrics
JF - Journal of Applied Econometrics
IS - 7
ER -