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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2411.09771 |
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| _version_ | 1866913578635755520 |
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| author | Waisman, Caio |
| author_facet | Waisman, Caio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09771 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bayesian estimation of finite mixtures of Tobit models Waisman, Caio Econometrics Applications 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. |
| title | Bayesian estimation of finite mixtures of Tobit models |
| topic | Econometrics Applications |
| url | https://arxiv.org/abs/2411.09771 |