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The Recursive Thick Frontier Approach to Estimating Efficiency

  • Wagenvoort, Rien

    ()

    (European Investment Bank, Economic and Financial Studies)

  • Schure, Paul

    (European University Institute in Florence)

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.

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Paper provided by European Investment Bank, Economics Department in its series Economic and Financial Reports with number 1999/2.

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Length: 18 pages
Date of creation: 01 Jul 1999
Date of revision:
Handle: RePEc:ris:eibefr:1999_002
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  1. 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.
  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. Breusch, T S & Pagan, A R, 1980. "The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics," Review of Economic Studies, Wiley Blackwell, vol. 47(1), pages 239-53, January.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
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