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Semiparametric Bayesian inference for stochastic frontier models
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UNSPECIFIED. (2004) Semiparametric Bayesian inference for stochastic frontier models. JOURNAL OF ECONOMETRICS, 123 (1). pp. 121-152. ISSN 0304-4076
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Official URL: http://dx.doi.org/10.1016/j.jeconom.2003.11.001
Abstract
In this paper we propose a semiparametric Bayesian framework for the analysis of stochastic frontiers and efficiency measurement. The distribution of inefficiencies is modelled nonparametrically through a Dirichlet process prior. We suggest prior distributions and implement a Bayesian analysis through an efficient Markov chain Monte Carlo sampler, which allows us to deal with practically relevant sample sizes. We also consider the case where the efficiency distribution varies with firm characteristics. The methodology is applied to a cost frontier, estimated from a panel data set on 382 U.S. hospitals. (C) 2003 Elsevier B.V. All rights reserved.
| Item Type: | Journal Article |
|---|---|
| Subjects: | H Social Sciences > HC Economic History and Conditions Q Science > QA Mathematics H Social Sciences |
| Journal or Publication Title: | JOURNAL OF ECONOMETRICS |
| Publisher: | ELSEVIER SCIENCE SA |
| ISSN: | 0304-4076 |
| Date: | November 2004 |
| Volume: | 123 |
| Number: | 1 |
| Number of Pages: | 32 |
| Page Range: | pp. 121-152 |
| Identification Number: | 10.1016/j.jeconom.2003.11.001 |
| Publication Status: | Published |
| URI: | http://wrap.warwick.ac.uk/id/eprint/7996 |
Data sourced from Thomson Reuters' Web of Knowledge
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