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The Recursive Thick Frontier Approach to Estimating Efficiency

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  • Wagenvoort, Rien

    () (European Investment Bank, Economic and Financial Studies)

  • Schure, Paul

    (European University Institute in Florence)

Abstract

The traditional econometric techniques for frontier models, namely the Stochastic Frontier Approach (SFA), the Thick Frontier Approach (TFA) and the Distribution Free Approach (DFA) have in common that they depend on a priori assumptions that are, whether feasible or not, difficult to test. This paper introduces the Recursive Thick Frontier Approach (RTFA) to the estimation of technology parameters when panel data is available. Our approach is based on the assertion that if deviations from the frontier of X-efficient companies are completely random then one must observe for this group of firms that the probability of being located either above or below the frontier is equal to one half. This hypothesis can be tested for panel data sets but requires sorting of the full sample into a group of X-inefficient firms and a group of X-efficient (best practice) firms. The cost frontier is estimated using only the observations of the latter category.

Suggested Citation

  • Wagenvoort, Rien & Schure, Paul, 1999. "The Recursive Thick Frontier Approach to Estimating Efficiency," Economic and Financial Reports 1999/2, European Investment Bank, Economics Department.
  • Handle: RePEc:ris:eibefr:1999_002
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    References listed on IDEAS

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    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    2. Hinloopen, Jeroen & Wagenvoort, Rien, 1997. "On the computation and efficiency of a HBP-GM estimator some simulation results," Computational Statistics & Data Analysis, Elsevier, vol. 25(1), pages 1-15, July.
    3. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," Review of Economic Studies, Oxford University Press, vol. 47(1), pages 239-253.
    4. 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.
    5. Kopp, Raymond J. & Mullahy, John, 1990. "Moment-based estimation and testing of stochastic frontier models," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 165-183.
    6. Wagenvoort, Rien & Schure, Paul, 1999. "Economies of Scale and Efficiency in European Banking: New Evidence," Economic and Financial Reports 1999/1, European Investment Bank, Economics Department.
    7. 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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    Cited by:

    1. O'Brien, Dermot & Wagenvoort, Rien, 2000. "Some Empirical Findings On The Characteristics Of Cost-Efficient Credit Institutions," Economic and Financial Reports 2000/1, European Investment Bank, Economics Department.
    2. Schure, Paul & Wagenvoort, Rien & O'Brien, Dermot, 2004. "The efficiency and the conduct of European banks: Developments after 1992," Review of Financial Economics, Elsevier, vol. 13(4), pages 371-396.
    3. Franz R. Hahn, 2004. "Measuring Performance. A Multiple-Stage Approach," WIFO Working Papers 228, WIFO.
    4. repec:wfo:wstudy:25688 is not listed on IDEAS
    5. Wagenvoort, Rien & Schure, Paul, 1999. "Economies of Scale and Efficiency in European Banking: New Evidence," Economic and Financial Reports 1999/1, European Investment Bank, Economics Department.

    More about this item

    Keywords

    Cost/Production Function; Thick Frontier Approach; X-efficiency;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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