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Semiparametric Bayesian Inference for Stochastic Frontier Models

Author

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  • Jim E. Griffin

    (University of Kent at Canterbury, UK)

  • Mark F.J. Steel

    (University of Kent at Canterbury, UK)

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.

Suggested Citation

  • Jim E. Griffin & Mark F.J. Steel, 2002. "Semiparametric Bayesian Inference for Stochastic Frontier Models," Econometrics 0209001, University Library of Munich, Germany, revised 18 Sep 2002.
    • Handle: RePEc:wpa:wuwpem:0209001
    Note: Type of Document - Acrobat PDF; prepared on IBM PC-LaTeX; to print on Postscript; pages: 26 ; figures: included
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    More about this item

    Keywords

    Dirichlet process; Efficiency measurement; Hospital cost frontiers; Markov chain Monte Carlo;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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