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A new approach to stochastic frontier estimation: DEA+

  • Dieter Gstach


    (Department of Economics, Vienna University of Economics & B.A.)

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    The outcome of a production process might not only deviate from a theoretical maximum due to inefficiency, but also because of non-controllable influences. This raises the issue of reliability of Data Envelopment Analysis in noisy environments. I propose to assume an i.i.d. data generating process with bounded noise component, so that the following approach is feasible: Use DEA to estimate a pseudo frontier first (nonparametric shape estimation). Next apply a ML- technique to the DEA-estimated efficiencies, to estimate the scalar value by which this pseudo-frontier must be shifted downward to get the true production frontier (location estimation). I prove, that this approach yields consistent estimates of the true frontier.

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    Paper provided by Vienna University of Economics and Business, Department of Economics in its series Department of Economics Working Papers with number wuwp039.

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    Date of creation: Jun 1996
    Date of revision:
    Handle: RePEc:wiw:wiwwuw:wuwp039
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    1. Simar, L., . "Aspects of statistical analysis in DEA-type frontier models," CORE Discussion Papers RP 1226, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. KNEIP, Alois & SIMAR, Léopold, 1995. "A General Framework for Frontier Estimation with Panel Data," CORE Discussion Papers 1995060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Greene, William H., 1980. "Maximum likelihood estimation of econometric frontier functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 27-56, May.
    4. Kittelsen,S.A.C., 1999. "Monte Carlo simulations of DEA efficiency measures and hypothesis tests," Memorandum 09/1999, Oslo University, Department of Economics.
    5. Sueyoshi, Toshiyuki, 1994. "Stochastic frontier production analysis: Measuring performance of public telecommunications in 24 OECD countries," European Journal of Operational Research, Elsevier, vol. 74(3), pages 466-478, May.
    6. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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