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Stochastic Frontier Models with Threshold Efficiency

  • Young Hoon Lee

    ()

    (Department of Economics, Sogang University, Seoul)

  • Sungwon Lee

    (Korea Development Institute)

This paper proposes a tail-truncated stochastic frontier model that allows for the truncation of technical efficiency from below. The truncation bound implies the inefficiency threshold for survival and also can be used as a measure of market competition. Specifically, this paper assumes a uniform distribution of technical inefficiency and derives the likelihood function. Even though this distributional assumption imposes a strong restriction that technical inefficiency has a uniform probability density over [0,], where is the threshold parameter, this model has two advantages: (i) the reduction of the number of parameters from the more complicated tail-truncated models allows better performance in numerical optimization; and (ii) the threshold parameter itself represents a degree of competition because the variance of technical inefficiency is dependent solely on the parameter. The Monte Carlo simulation results support the argument that this model approximates the distribution of inefficiency precisely.

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File Function: First version, 2011
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Paper provided by Research Institute for Market Economy, Sogang University in its series Working Papers with number 1205.

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Length: 23 pages
Date of creation: 2011
Date of revision:
Handle: RePEc:sgo:wpaper:1205
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Web page: http://home.sogang.ac.kr/sites/sgrime
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  1. David Good & M. Nadiri & Lars-Hendrik Röller & Robin Sickles, 1993. "Efficiency and productivity growth comparisons of European and U.S. Air carriers: A first look at the data," Journal of Productivity Analysis, Springer, vol. 4(1), pages 115-125, June.
  2. Chad Syverson, 2001. "Market Structure and Productivity: A Concrete Example," Working Papers 01-06, Center for Economic Studies, U.S. Census Bureau.
  3. Thomas J. Holmes & James A. Schmitz, Jr., 2010. "Competition and productivity: a review of evidence," Staff Report 439, Federal Reserve Bank of Minneapolis.
  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. Qu Feng & William C. Horrace, 2012. "Alternative technical efficiency measures: Skew, bias and scale," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(2), pages 253-268, 03.
  6. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
  7. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
  8. David A. Matsa, 2011. "Competition and Product Quality in the Supermarket Industry," The Quarterly Journal of Economics, Oxford University Press, vol. 126(3), pages 1539-1591.
  9. 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.
  10. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
  11. 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.
  12. Timothy Dunne & Shawn Klimek & James Schmitz, Jr., 2010. "Competition and Productivity: Evidence from the Post WWII U.S. Cement Industry," Working Papers 10-29, Center for Economic Studies, U.S. Census Bureau.
  13. Greene, William H., 1980. "Maximum likelihood estimation of econometric frontier functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 27-56, May.
  14. 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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