Comparing bayesian and corrected least-squares estimators in frontier production models
In the econometric approach to deterministic frontier production mod- els, the use of maximum likelihood estimation has major problems, because the model violates the usual regularity conditions that allow to establish the desirable asymptotic properties of the estimators. To avoid this diculty, we...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2009 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/83317 |
| Acceso en línea: | https://hdl.handle.net/11441/83317 |
| Access Level: | acceso abierto |
| Palabra clave: | Production models Bayesian Inference Gibbs sampling Cor- rected least-squares estimator. |
| Sumario: | In the econometric approach to deterministic frontier production mod- els, the use of maximum likelihood estimation has major problems, because the model violates the usual regularity conditions that allow to establish the desirable asymptotic properties of the estimators. To avoid this diculty, we may use other approachs. One of the methods most widely used is the corrected least-squares estimator. Alternatively, we can perform the Bayesian estimation using Gibbs sampling. In this paper, we make a comparative study of both approachs using simulation methods. We will conclude that the bayesian estimator has better properties in terms of bias and mean squared error, especially for the intercept term; this fact can result very important to estimate the individual eciencies, which is one of the main objectives of these models. |
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