Bayesian Estimation and Smoothing of the Baseline Hazard in Discrete Time Duration Models
提出一种贝叶斯方法，用于离散时间风险模型中基线风险的估计和平滑，通过吉布斯抽样估计多期probit模型，并利用平滑先验对基线风险进行平滑处理，最后应用于加拿大失业保险资格规则对就业持续时间的影响研究。
This paper proposes a Bayesian approach for estimating and smoothing the baseline hazard in a discrete time hazard model. The hazard model is specified as a multiperiod probit model and estimated using a Gibbs sampler with data augmentation. The baseline hazard specification is smoothed using the smoothness priors introduced by Shiller (1973). The methods proposed in this paper are then used to study the effect of Canadian Unemployment Insurance eligibility rules on employment durations from New Brunswick, Canada. © 2000 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology