Dynamic Discrete-Time Duration Models
Dynamic Discrete-Time Duration Models
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发表时间：
1997
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通讯作者：
L. Fahrmeir;L. Knorr‐Held
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作者：
L. Fahrmeir;L. Knorr‐Held
离散{时间分组持续时间数据，具有一种或多种类型的终止事件，经常在社会科学或经济学中观察到。在本文中，我们建议和讨论动态模型，灵活的贝叶斯非参数分析等数据。这些模型允许同时合并和估计基线风险和随时间变化的协变量效应，而不施加特定的参数形式。探索时变效应的可能性的方法，例如国籍或失业保险金对再就业可能性的影响，最近引起了越来越多的兴趣。我们的建模和估计方法是完全贝叶斯，并利用马尔可夫链蒙特卡罗（MCMC）模拟技术。通过对失业持续时间数据的详细分析，以全职、兼职和其他原因作为终止事件，说明了我们的方法，并展示了如何使用它们来获得重新定义的结果和解释。
Discrete{time grouped duration data, with one or multiple types of terminating events, are often observed in social sciences or economics. In this paper we suggest and discuss dynamic models for exible Bayesian nonparametric analysis of such data. These models allow simultaneous incorporation and estimation of baseline hazards and time{varying covariate eeects, without imposing particular parametric forms. Methods for exploring the possibility of time{varying eeects, as for example the impact of nationality or unemployment insurance beneets on the probability of re{employment, have recently gained increasing interest. Our modeling and estimation approach is fully Bayesian and makes use of Markov Chain Monte Carlo (MCMC) simulation techniques. A detailed analysis of unemployment duration data, with full{time job, part{time job and other causes as terminating events, illustrates our methods and shows how they can be used to obtain reened results and interpretations.