Semiparametric Bayesian inference for stochastic frontier models

Griffin, Jim E. and Steel, Mark F.J. (2004) Semiparametric Bayesian inference for stochastic frontier models. Journal of Econometrics, 123 (1). pp. 121-152. ISSN 0304-4076. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not available from this repository. (Contact us about this Publication)
Official URL


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.

Item Type: Article
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: 28 Apr 2014 15:06
Resource URI: (The current URI for this page, for reference purposes)
  • Depositors only (login required):