Bayesian estimation of finite mixtures of Tobit models

TL;DR

This paper introduces a Bayesian MCMC method for estimating finite mixtures of Tobit models, offering enhanced flexibility especially with censored data, demonstrated through simulations and real-world applications in labor and healthcare.

Contribution

The paper presents a simple, flexible Bayesian estimation approach for finite mixture Tobit models, combining Gibbs sampling and data augmentation, applicable to various empirical contexts.

Findings

Method improves model fit with censored data

Flexibility can significantly alter results

Applicable to labor and healthcare data

Abstract

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.