IDEAS home Printed from https://ideas.repec.org/p/sgo/wpaper/1205.html
   My bibliography  Save this paper

Stochastic Frontier Models with Threshold Efficiency

Author

Listed:
  • Young Hoon Lee

    () (Department of Economics, Sogang University, Seoul)

  • Sungwon Lee

    (Korea Development Institute)

Abstract

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.

Suggested Citation

  • Young Hoon Lee & Sungwon Lee, 2011. "Stochastic Frontier Models with Threshold Efficiency," Working Papers 1205, Research Institute for Market Economy, Sogang University.
  • Handle: RePEc:sgo:wpaper:1205
    as

    Download full text from publisher

    File URL: ftp://163.239.156.99/wpaper/LYH_RIME_2012-5.pdf
    File Function: First version, 2011
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Greene, William H., 1980. "Maximum likelihood estimation of econometric frontier functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 27-56, May.
    3. 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.
    4. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
    5. Chad Syverson, 2004. "Market Structure and Productivity: A Concrete Example," Journal of Political Economy, University of Chicago Press, vol. 112(6), pages 1181-1222, December.
    6. 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.
    7. 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.
    8. 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 435-444, June.
    9. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    10. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    11. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    12. 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, March.
    13. Thomas J. Holmes & James A. Schmitz, 2010. "Competition and Productivity: A Review of Evidence," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 619-642, September.
    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.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Stochastic frontier; technical efficiency; threshold inefficiency; uniform distribution; productivity distribution;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sgo:wpaper:1205. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jung Hur). General contact details of provider: http://edirc.repec.org/data/risogkr.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.