Bayesian estimation and smoothing of the baseline hazard in discrete time duration models

• 成果类型: Article
• 署名作者: Campolieti, M
• 署名单位: University of Toronto
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/003465300559019
• 发表日期: 2000-11
• 页码: 685-694
• 关键词: unemployment-insurance; likelihood; regression

摘要

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.