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A Recursive Thick Frontier Approach To Estimating Production Efficiency

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Abstract

We introduce a new panel data estimation technique for cost and production functions: the Recursive Thick Frontier Approach (RTFA). RTFA has two advantages over existing thick frontier methods. First, technical inefficiency is allowed to be dependent on the explanatory variables of the frontier model. Secondly, no distributional assumptions are imposed on the inefficiency component of the error term. We show by means of simulation experiments that RTFA can outperform the popular stochastic frontier approach (SFA) and the “within” OLS estimator for realistic parameterisations of the productivity model.

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Bibliographic Info

Paper provided by Department of Economics, University of Victoria in its series Econometrics Working Papers with number 0503.

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Length: 26 pages
Date of creation: 11 Mar 2005
Date of revision:
Handle: RePEc:vic:vicewp:0503

Note: ISSN 1485-6441
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Fax: (250)721-6214
Web page: http://web.uvic.ca/econ
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Keywords: Technical Efficiency; Efficiency Measurement; Frontier Production Functions; Recursive Thick Frontier Approach;

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  1. Cazals, Catherine & Florens, Jean-Pierre & Simar, Leopold, 2002. "Nonparametric frontier estimation: a robust approach," Journal of Econometrics, Elsevier, vol. 106(1), pages 1-25, January.
  2. 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.
  3. Schmidt, Peter, 1976. "On the Statistical Estimation of Parametric Frontier Production Functions," The Review of Economics and Statistics, MIT Press, vol. 58(2), pages 238-39, May.
  4. Allen N. Berger & David B. Humphrey, 1992. "Measurement and Efficiency Issues in Commercial Banking," NBER Chapters, in: Output Measurement in the Service Sectors, pages 245-300 National Bureau of Economic Research, Inc.
  5. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
  6. Greene, William H., 1980. "Maximum likelihood estimation of econometric frontier functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 27-56, May.
  7. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-74, October.
  8. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
  9. Léopold Simar, 2003. "Detecting Outliers in Frontier Models: A Simple Approach," Journal of Productivity Analysis, Springer, vol. 20(3), pages 391-424, November.
  10. 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.
  11. Kalirajan, K P & Obwona, M B, 1994. "Frontier Production Function: The Stochastic Coefficients Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 56(1), pages 87-96, February.
  12. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
  13. 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.
  14. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
  15. 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.
  16. 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.
  17. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-44, June.
  18. 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.
  19. Kalirajan, K P & Shand, R T, 1999. " Frontier Production Functions and Technical Efficiency Measures," Journal of Economic Surveys, Wiley Blackwell, vol. 13(2), pages 149-72, April.
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Cited by:
  1. Jorge David Quinteo Otero & William Orlando Prieto Bustos & Fernando Barrios Aguirre & Laura Elena Leviller Guardo, 2008. "Determinantes de la eficiencia técnica en las empresas colombianas, 2001-2004," REVISTA SEMESTRE ECONÓMICO, UNIVERSIDAD DE MEDELLÍN.

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