TY - JOUR
T1 - Analysing plant closure effects using time-varying mixture-of-experts Markov chain clustering
AU - Frühwirth-Schnatter, Sylvia
AU - Pittner, Stefan
AU - Weber, Andrea
AU - Winter-Ebmer, Rudolf
N1 - Publisher Copyright:
© Institute of Mathematical Statistics, 2018.
PY - 2018/9
Y1 - 2018/9
N2 - 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.
AB - 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.
KW - Inhomogeneous Markov chains
KW - Markov chain Monte Carlo
KW - Multinomial logit
KW - Panel data
KW - Transition data
UR - http://www.scopus.com/inward/record.url?scp=85053352791&partnerID=8YFLogxK
U2 - 10.1214/17-AOAS1132
DO - 10.1214/17-AOAS1132
M3 - Article
AN - SCOPUS:85053352791
SN - 1932-6157
VL - 12
SP - 1796
EP - 1830
JO - Annals of Applied Statistics
JF - Annals of Applied Statistics
IS - 3
ER -