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Another Approach to Data Envelopment Analysis in Noisy Environments: DEA+

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  • Dieter Gstach

Abstract

In this paper a DEA+ labeled approach for efficiency measurement in the stochastic case is presented along with a consistency proof and some preliminary evidence illustrating the small sample performance. DEA+ can basically handle multi-output technologies like standard DEA but allows to filter noise, that might have disturbed production and unlike a related approach does not require panel data. Consistency of DEA+ relies on the assumption of i.i.d. distributed and bounded noise and requires radial efficiency measurement. First Monte Carlo experiments show that a DEA+ based average inefficiency estimator performs well for samples of size n=100 in one-output, two-input settings compared to the corresponding Stochastic Frontier Estimator. Sensitivity of DEA+ performance with respect to parametrization of noise is weak, but higher noise contribution requires much larger sample size for satisfactory results. Copyright Kluwer Academic Publishers 1998

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  • Dieter Gstach, 1998. "Another Approach to Data Envelopment Analysis in Noisy Environments: DEA+," Journal of Productivity Analysis, Springer, vol. 9(2), pages 161-176, March.
    • Handle: RePEc:kap:jproda:v:9:y:1998:i:2:p:161-176
    DOI: 10.1023/A:1018312801700
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    References listed on IDEAS

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    1. 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.
    2. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    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. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    5. Patrick L. Brockett & Boaz Golany, 1996. "Using Rank Statistics for Determining Programmatic Efficiency Differences in Data Envelopment Analysis," Management Science, INFORMS, vol. 42(3), pages 466-472, March.
    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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