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September 2018 Analysing plant closure effects using time-varying mixture-of-experts Markov chain clustering
Sylvia Frühwirth-Schnatter, Stefan Pittner, Andrea Weber, Rudolf Winter-Ebmer
Ann. Appl. Stat. 12(3): 1796-1830 (September 2018). DOI: 10.1214/17-AOAS1132

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.

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Sylvia Frühwirth-Schnatter. Stefan Pittner. Andrea Weber. Rudolf Winter-Ebmer. "Analysing plant closure effects using time-varying mixture-of-experts Markov chain clustering." Ann. Appl. Stat. 12 (3) 1796 - 1830, September 2018. https://doi.org/10.1214/17-AOAS1132

Information

Received: 1 September 2016; Revised: 1 December 2017; Published: September 2018
First available in Project Euclid: 11 September 2018

zbMATH: 06979652
MathSciNet: MR3852698
Digital Object Identifier: 10.1214/17-AOAS1132

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.12 • No. 3 • September 2018
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