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A Bayesian analysis of multiple-output production frontiers

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Author Info
Carmen Fernandez
Gary Koop ()
M. F. J. Steel

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Abstract

In this paper we develop Bayesian tools for estimating multi-output production frontiers in applications where only input and output data are available. Firm-specific inefficiency is measured relative to this frontier. Our work has important differences from the existing literature, which either assumes a classical econometric perspective with restrictive functional form assumptions, or a non-stochastic approach which directly estimates the output distance function. Bayesian inference is implemented using a Markov Chain Monte Carlo algorithm. A banking application shows the ease and practicality of our approach.

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Publisher Info
Paper provided by Edinburgh School of Economics, University of Edinburgh in its series ESE Discussion Papers with number 21.

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Date of creation: Oct 2004
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Handle: RePEc:edn:esedps:21

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  1. Goto, Mika & Makhija, Anil K., 2007. "The Impact of Competition and Corporate Structure on Productive Efficiency: The Case of the U.S. Electric Utility Industry, 1990-2004," Working Paper Series 2007-10, Ohio State University, Charles A. Dice Center for Research in Financial Economics. [Downloadable!]
  2. Kelvin Balcombe & Iain Fraser & Jae H. Kim, 2006. "Estimating technical efficiency of Australian dairy farms using alternative frontier methodologies," Applied Economics, Taylor and Francis Journals, vol. 38(19), pages 2221-2236, October. [Downloadable!] (restricted)
  3. Efthymios G. Tsionas, 2006. "Inference in dynamic stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 669-676. [Downloadable!]
  4. Jose T.A.S. Ferreira & Mark F.J. Steel, 2004. "Model Comparison of Coordinate-Free Multivariate Skewed Distributions with an Application to Stochastic Frontiers," Econometrics 0404005, EconWPA. [Downloadable!]
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  5. Gary Koop & Lise Tole, 2008. "What is the environmental performance of firms overseas? An empirical investigation of the global gold mining industry," Journal of Productivity Analysis, Springer, vol. 30(2), pages 129-143, October. [Downloadable!] (restricted)
    Other versions:
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