Bayesian inference for the mover–stayer model in continuous time with an application to labour market transition data
提出连续时间移动-停留模型的贝叶斯推断方法，用于分析离散时间收集的劳动力市场转型数据，重点介绍了重要性抽样和吉布斯抽样两种算法，并利用法国劳动力调查数据估计了转型强度和停留者比例。
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.