Analysing Plant Closure Effects Using Time-Varying Mixture-of-Experts Markov Chain Clustering

Sylvia Frühwirth-Schnatter (), Stefan Pittner, Andrea Weber and Rudolf Winter-Ebmer
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Sylvia Frühwirth-Schnatter: Department of Applied Statistics, Johannes Kepler University Linz, Austria, http://www.ifas.jku.at/e2571/e2626/index_ger.html

No 2016-06, CDL Aging, Health, Labor working papers from The Christian Doppler (CD) Laboratory Aging, Health, and the Labor Market, Johannes Kepler University Linz, Austria

Abstract: In this paper, we study data on discrete labor market transitions from Austria. In particular, we follow the careers of workers who experience a job displacement due to plant closure and observe { over a period of forty quarters { whether these workers manage to return to a steady career path. To analyse these discrete-valued panel data, we develop and apply a new method of Bayesian Markov chain clustering analysis based on inhomogeneous rst order Markov transition processes with time-varying transition matrices. In addition, a mixture-of-experts approach allows us to model the prior probability to belong to a certain cluster in dependence of a set of covariates via a multinomial logit model. Our cluster analysis identi es ve career patterns after plant closure and reveals that some workers cope quite easily with a job loss whereas others su er large losses over extended periods of time.

Keywords: Transition data; Markov Chain Monte Carlo; Multinomial Logit; Panel data; Inhomogeneous Markov chains (search for similar items in EconPapers)
Pages: 29 pages
Date: 2016-09
Note: English
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Related works:
Working Paper: Analysing Plant Closure Effects Using Time-Varying Mixture-of-Experts Markov Chain Clustering (2016)
Working Paper: Analysing Plant Closure Effects Using Time-Varying Mixture-of-Experts Markov Chain Clustering (2016)
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