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On the generation of a regular multi-input multi-output technology using parametric output distance functions

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Author Info
Sergio Perlman
Daniel Santin

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Abstract

Monte-Carlo experimentation is a well-known approach to test the performance of alternative methodologies under different hypothesis. In the frontier analysis framework, whatever parametric or non-parametric methods tested, most experiments have been developed up to now assuming single output multi-input production functions and data generated using a Cobb- Douglas technology. The aim of this paper is to show how reliable multi-output multi-input production data can be generated using a parametric output distance function approach. A flexible translog technology is used for this purpose that satisfies regularity conditions. Two meaningful outcomes of this analysis are the identification of a valid range of parameters values satisfying monotonicity and curvature restrictions and of a rule of thumb to be applied in empirical studies.

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Publisher Info
Paper provided by Centre de Recherche en Economie Publique et de la Population (CREPP) (Research Center on Public and Population Economics) HEC-Management School, University of Liège in its series CREPP Working Papers with number 0507.

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Date of creation: 2005
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Handle: RePEc:rpp:wpaper:0507

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Related research
Keywords: Output distance function; technical efficiency; Monte-Carlo experiments.;

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models

References listed on IDEAS
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  1. Gong, Byeong-Ho & Sickles, Robin C., 1992. "Finite sample evidence on the performance of stochastic frontiers and data envelopment analysis using panel data," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 259-284. [Downloadable!] (restricted)
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  2. Ruggiero, John, 1998. "Non-discretionary inputs in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 111(3), pages 461-469, December. [Downloadable!] (restricted)
  3. Banker, Rajiv D. & Gadh, Vandana M. & Gorr, Wilpen L., 1993. "A Monte Carlo comparison of two production frontier estimation methods: Corrected ordinary least squares and data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 67(3), pages 332-343, June. [Downloadable!] (restricted)
  4. Battese, George E. & Corra, Greg. S., 1977. "Estimation Of A Production Frontier Model: With Application To The Pastoral Zone Of Eastern Australia," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 21(03), December. [Downloadable!]
  5. O'Donnell, Christopher J. & Coelli, Timothy J., 2005. "A Bayesian approach to imposing curvature on distance functions," Journal of Econometrics, Elsevier, vol. 126(2), pages 493-523, June. [Downloadable!] (restricted)
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  6. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-44, June. [Downloadable!] (restricted)
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