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Bayesian inference for the mover–stayer model in continuous time with an application to labour market transition data
Author(s) -
Fougère Denis,
Kamionka Thierry
Publication year - 2003
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.727
Subject(s) - gibbs sampling , inference , bayesian probability , sampling (signal processing) , bayesian inference , econometrics , discrete time and continuous time , transition (genetics) , computer science , mathematics , statistics , artificial intelligence , biochemistry , chemistry , filter (signal processing) , computer vision , gene
Abstract This paper presents Bayesian inference procedures for the continuous time mover–stayer model applied to labour market transition data collected in discrete time. These methods allow us to derive the probability of embeddability of the discrete‐time modelling with the continuous‐time one. A special emphasis is put on two alternative procedures, namely the importance sampling algorithm and a new Gibbs sampling algorithm. Transition intensities, proportions of stayers and functions of these parameters are then estimated with the Gibbs sampling algorithm for individual transition data coming from the French Labour Force Surveys collected over the period 1986–2000. Copyright © 2003 John Wiley & Sons, Ltd.