The Annals of Applied Statistics

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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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.

Article information

Ann. Appl. Stat., Volume 12, Number 3 (2018), 1796-1830.

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

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Transition data Markov chain Monte Carlo multinomial logit panel data inhomogeneous Markov chains


Frühwirth-Schnatter, Sylvia; Pittner, Stefan; Weber, Andrea; Winter-Ebmer, Rudolf. Analysing plant closure effects using time-varying mixture-of-experts Markov chain clustering. Ann. Appl. Stat. 12 (2018), no. 3, 1796--1830. doi:10.1214/17-AOAS1132.

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