This paper outlines a Bayesian approach to estimate finite mixtures of Tobit models. The method consists of an MCMC approach that combines Gibbs sampling with data augmentation and is simple to implement. I show through simulations that the flexibility provided by this method is especially helpful when censoring is not negligible. In addition, I demonstrate the broad utility of this methodology with applications to a job training program, labor supply, and demand for medical care. I find that this approach allows for non-trivial additional flexibility that can alter results considerably and beyond improving model fit.

翻译：本文概述了一种用于估计托宾模型有限混合的贝叶斯方法。该方法采用结合吉布斯抽样与数据增强的马尔可夫链蒙特卡洛（MCMC）框架，实现过程简洁。通过模拟实验，本文证明该方法提供的灵活性在删失效应不可忽略时尤为有效。此外，本文通过就业培训项目、劳动力供给及医疗需求三个实证案例，展示了该方法的广泛适用性。研究发现，该方法能提供显著的额外灵活性，不仅可改善模型拟合度，更能从根本上改变分析结论。