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A new approach to stochastic frontier estimation: DEA+

Author

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  • Gstach, Dieter
Abstract
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. (author's abstract)

Suggested Citation

  • Gstach, Dieter, 1996. "A new approach to stochastic frontier estimation: DEA+," Department of Economics Working Paper Series 39, WU Vienna University of Economics and Business.
    • Handle: RePEc:wiw:wus005:298
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    References listed on IDEAS

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    1. 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.
    2. Gstach, Dieter, 1995. "Comparing Structural Efficiency of Unbalanced Subsamples: A Resampling Adaptation of Data Envelopment Analysis," Empirical Economics, Springer, vol. 20(3), pages 531-542.
    3. Kittelsen,S.A.C., 1999. "Monte Carlo simulations of DEA efficiency measures and hypothesis tests," Memorandum 09/1999, Oslo University, Department of Economics.
    4. 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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