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Stochastic Frontier Production Function With Errors-In-Variables

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  • Dhawan, Rajeev

    ()
    (Anderson Graduate School of Management)

  • Jochumzen, Peter

    ()
    (Department of Economics, Lund University)

Abstract

This paper develops a procedure for estimating parameters of a cross-sectional stochastic frontier production function when the factors of production suffer from measurement errors. Specifically, we use Fuller's (1987) reliability ratio concept to develop an estimator for the model in Aigner et al (1977). Our Monte-Carlo simulation exercise illustrates the direction and the severity of bias in the estimates of the elasticity parameters and the returns to scale feature of the production function when using the traditional maximum-likelihood estimator (MLE) in presence of measurement errors. In contrast the reliability ratio based estimator consistently estimates these parameters even under extreme degree of measurement errors. Additionally, estimates of firm level technical efficiency are severely biased for traditional MLE compared to reliability ratio estimator, rendering inter-firm efficiency comparisons infeasible. The seriousness of measurement errors in a practical setting is demonstrated by using data for a cross-section of publicly traded U.S. corporations.

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Bibliographic Info

Paper provided by Lund University, Department of Economics in its series Working Papers with number 1999:007.

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Length: 33 pages
Date of creation: 29 Sep 1999
Date of revision:
Handle: RePEc:hhs:lunewp:1999_007

Contact details of provider:
Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund,Sweden
Phone: +46 +46 222 0000
Fax: +46 +46 2224613
Web page: http://www.nek.lu.se/en
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Related research

Keywords: Errors-In-Variables; Stochastic Frontier; Technical Efficiency; Reliability Ratio;

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References

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  1. 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.
  2. 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.
  3. Kumbhakar, Subal C, 1988. "On the Estimation of Technical and Allocative Inefficiency Using Stochastic Frontier Functions: The Case of U.S. Class 1 Railroads," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 29(4), pages 727-43, November.
  4. Mark L. Gertler & R. Glenn Hubbard, 1989. "Financial Factors in Business Fluctuations," NBER Working Papers 2758, National Bureau of Economic Research, Inc.
  5. Kumbhakar, Subal C & Hjalmarsson, Lennart, 1995. "Labour-Use Efficiency in Swedish Social Insurance Offices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(1), pages 33-47, Jan.-Marc.
  6. Beckers, Dominique E. & Hammond, Christopher J., 1987. "A tractable likelihood function for the normal-gamma stochastic frontier model," Economics Letters, Elsevier, vol. 24(1), pages 33-38.
  7. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
  8. Paul W. Bauer, 1988. "Decomposing TFP growth in the presence of cost inefficiency, nonconstant returns to scale, and technological progress," Working Paper 8813, Federal Reserve Bank of Cleveland.
  9. Edgerton, David & Jochumzen, Peter, 2003. "Estimation in Binary Choice Models with Measurement Errors," Working Papers 2003:4, Lund University, Department of Economics, revised 07 Jul 2003.
  10. 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.
  11. Klepper, Steven & Leamer, Edward E, 1984. "Consistent Sets of Estimates for Regressions with Errors in All Variables," Econometrica, Econometric Society, vol. 52(1), pages 163-83, January.
  12. Murphy, Kevin M & Topel, Robert H, 2002. "Estimation and Inference in Two-Step Econometric Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 88-97, January.
  13. 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.
  14. Rajeev Dhawan, 1997. "Asymmetric Information and Debt Financing: Hie Empirical Importance of Size and Balance Sheet Factors," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 4(2), pages 189-202.
  15. Kumbhakar, Subal C, 1987. "Production Frontiers and Panel Data: An Application to U.S. Class 1 Railroads," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(2), pages 249-55, April.
  16. Bauer, Paul W., 1990. "Recent developments in the econometric estimation of frontiers," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 39-56.
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