A comparison between maximum likelihood and Bayesian estimation of stochastic frontier production models
In this paper, the finite sample properties of the maximum likelihood and Bayesian estimators of the half-normal stochastic frontier production function are analyzed and compared through a Monte Carlo study. The results show that the Bayesian estimator should be used in preference to the maximum lik...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2014 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/70548 |
| Acceso en línea: | https://hdl.handle.net/11441/70548 https://doi.org/10.1080/03610918.2012.743564 |
| Access Level: | acceso abierto |
| Palabra clave: | Bayesian estimator Maximum likelihood Monte Carlo Stochastic frontier |
| Sumario: | In this paper, the finite sample properties of the maximum likelihood and Bayesian estimators of the half-normal stochastic frontier production function are analyzed and compared through a Monte Carlo study. The results show that the Bayesian estimator should be used in preference to the maximum likelihood owing to the fact that the mean square error performance is substantially better in the Bayesian framework |
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