Analysing plant closure effects using time-varying mixture-of-experts Markov chain clustering
Sylvia Frühwirth-Schnatter, Stefan Pittner, Andrea Weber, Rudolf Winter-Ebmer
The Annals of Applied Statistics · 2018 · Volume 12 · Issue 3
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 40 quarters—whether these workers manage to return to a steady career path. To analyse these discrete-valued panel data, we 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 probability of belonging to a certain cluster as depending on 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
Inhomogeneous Markov chains, Markov chain Monte Carlo, multinomial logit, panel data, Transition data
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