Semiparametric Bayesian Inference for Stochastic Frontier Models

Jim Griffin and Mark Steel

Econometrics from University Library of Munich, Germany

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 allow for the efficiency distribution to vary with firm characteristics. The methodology is applied to a cost frontier, estimated from a panel data set on 382 U.S. hospitals.

Keywords: Dirichlet process; Efficiency measurement; Hospital cost frontiers; Markov chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 C14 C23 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2002-09-18, Revised 2002-09-18
New Economics Papers: this item is included in nep-ecm
Note: Type of Document - Acrobat PDF; prepared on IBM PC-LaTeX; to print on Postscript; pages: 26 ; figures: included
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Semiparametric Bayesian inference for stochastic frontier models (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpem:0209001

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