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DEA Problems under Geometrical or Probability Uncertainties of Sample Data

Author

Listed:
  • Althaler, Karl S.

    (Institute for Advanced Studies, Vienna)

  • Slavova, Tatjana

    (Institute for Advanced Studies, Vienna)

Abstract

This paper discusses the theoretical and practical aspects of new methods for solving DEA problems under real-life geometrical uncertainty and probability uncertainty of sample data. The proposed minimax approach to solve problems with geometrical uncertainty of sample data involves an implementation of linear programming or minimax optimization, whereas the problems with probability uncertainty of sample data are solved through implementing of econometric and new stochastic optimization methods, using the stochastic frontier functions estimation.

Suggested Citation

  • Althaler, Karl S. & Slavova, Tatjana, 2000. "DEA Problems under Geometrical or Probability Uncertainties of Sample Data," Economics Series 89, Institute for Advanced Studies.
  • Handle: RePEc:ihs:ihsesp:89
    as

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    File URL: http://www.ihs.ac.at/publications/eco/es-89.pdf
    File Function: First version, 2000
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    References listed on IDEAS

    as
    1. Forsund, Finn R. & Lovell, C. A. Knox & Schmidt, Peter, 1980. "A survey of frontier production functions and of their relationship to efficiency measurement," Journal of Econometrics, Elsevier, vol. 13(1), pages 5-25, May.
    2. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    3. 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.
    4. Lee, Lung-Fei, 1993. "Asymptotic Distribution of the Maximum Likelihood Estimator for a Stochastic Frontier Function Model with a Singular Information Matrix," Econometric Theory, Cambridge University Press, vol. 9(03), pages 413-430, June.
    5. Coelli, T. J., 1992. "A computer program for frontier production function estimation : Frontier version 2.0," Economics Letters, Elsevier, vol. 39(1), pages 29-32, May.
    6. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    7. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    8. Hughes, Michael D, 1988. "A Stochastic Frontier Cost Function for Residential Child Care Provision," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 3(3), pages 203-214, July-Sept.
    9. Schmidt, Peter & Knox Lovell, C. A., 1979. "Estimating technical and allocative inefficiency relative to stochastic production and cost frontiers," Journal of Econometrics, Elsevier, vol. 9(3), pages 343-366, February.
    10. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
    11. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    12. Bauer, Paul W., 1990. "Recent developments in the econometric estimation of frontiers," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 39-56.
    13. 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.
    14. Seiford, Lawrence M. & Thrall, Robert M., 1990. "Recent developments in DEA : The mathematical programming approach to frontier analysis," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 7-38.
    15. Reifschneider, David & Stevenson, Rodney, 1991. "Systematic Departures from the Frontier: A Framework for the Analysis of Firm Inefficiency," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 32(3), pages 715-723, August.
    16. Kumbhakar, Subal C & Ghosh, Soumendra & McGuckin, J Thomas, 1991. "A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in U.S. Dairy Farms," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(3), pages 279-286, July.
    17. Yunos, Jamaluddin Mohd & Hawdon, David, 1997. "The efficiency of the National Electricity Board in Malaysia: An intercountry comparison using DEA," Energy Economics, Elsevier, vol. 19(2), pages 255-269, May.
    18. Battese, George E. & Coelli, Tim J. & Colby, T.C., 1989. "Estimation of Frontier Production Functions and the Efficiencies of Indian Farms Using Panel Data from ICRISAT's Village Level Studies," 1989 Conference (33rd), February 7-9, 1989, Christchurch, New Zealand 144383, Australian Agricultural and Resource Economics Society.
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    Cited by:

    1. Tatjana Slavova, 2008. "A rank order and efficiency evaluation of the EU regions in a social framework," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 35(4), pages 339-367, September.

    More about this item

    Keywords

    DEA; Sample data uncertainty; Linear programming; Minimax optimization; Stochastic optimization; Stochastic frontier functions;

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • H72 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Budget and Expenditures

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