Griffin, J.E. and Steel, M.F.J. (2004) Semiparametric Bayesian inference for stochastic frontier models. JOURNAL OF ECONOMETRICS, 123 (1). pp. 121-152. ISSN 0304-4076.
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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.
|Uncontrolled keywords:||Dirichlet process; Efficiency measurement; Hospital cost frontiers; Markov chain Monte Carlo|
|Subjects:||Q Science > QA Mathematics (inc Computing science)|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics|
|Depositing User:||Judith Broom|
|Date Deposited:||25 Sep 2008 23:41|
|Last Modified:||14 Jan 2010 14:28|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/7775 (The current URI for this page, for reference purposes)|
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