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

Sylvia Frwühwirth-Schnatter, Stefan Pittner, Andrea Weber and Rudolf Winter-Ebmer
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Sylvia Frwühwirth-Schnatter: Institute for Statistics and Mathematics, Vienna University of Economics and Business
Stefan Pittner: Institute for Statistics and Mathematics, Vienna University of Economics and Business

No 324, Economics Series from Institute for Advanced Studies

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 first 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 identifies five career patterns after plant closure and reveals that some workers cope quite easily with a job loss whereas others suffer 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: 28 pages
Date: 2016-10
New Economics Papers: this item is included in nep-dcm
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https://irihs.ihs.ac.at/id/eprint/4078 First version, 2016 (application/pdf)

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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