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Predicting technical effciency in stochastic production frontier models in the presence of misspecification: a Monte-Carlo analysis

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  • Konstantinos Giannakas
  • Kien Tran
  • Vangelis Tzouvelekas

Abstract

This paper provides a theoretical explanation for the sensitivity of technical efficiency measures to the choice of functional specification in stochastic production frontier models. It is shown that inappropriate functional specifications translate into a misspecification in the conditional mean of the stochastic frontier regression model. This misspecification, in turn, results in estimates of technical efficiency, confidence intervals and production elasticities being biased, even asymptotically. Monte-Carlo simulations reveal that the severity of the bias depends on the functional specification and the percentage contribution of the variance of technical inefficiency to the total variance of the composed errors.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Economics.

Volume (Year): 35 (2003)
Issue (Month): 2 ()
Pages: 153-161

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Handle: RePEc:taf:applec:v:35:y:2003:i:2:p:153-161

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  1. Appelbaum, Elie, 1979. "On the Choice of Functional Forms," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 20(2), pages 449-58, June.
  2. Kumbhakar, Subal C. & Heshmati, Almas & Hjalmarsson, Lennart, 1997. "Temporal patterns of technical efficiency: Results from competing models," International Journal of Industrial Organization, Elsevier, vol. 15(5), pages 597-616, August.
  3. Suzhen Zhu & Paul Ellinger & C. Richard Shumway, 1995. "The choice of functional form and estimation of banking inefficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 2(10), pages 375-379.
  4. Konstantinos Giannakas & Kien Tran & Vangelis Tzouvelekas, 2000. "Efficiency, technological change and output growth in Greek olive growing farms: a Box-Cox approach," Applied Economics, Taylor & Francis Journals, vol. 32(7), pages 909-916.
  5. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
  6. Kumbhakar,Subal C. & Lovell,C. A. Knox, 2003. "Stochastic Frontier Analysis," Cambridge Books, Cambridge University Press, number 9780521666633, October.
  7. Lee, Lung-Fei, 1983. "A test for distributional assumptions for the stochastic frontier functions," Journal of Econometrics, Elsevier, vol. 22(3), pages 245-267, August.
  8. Fare, Rolf & Knox Lovell, C. A., 1978. "Measuring the technical efficiency of production," Journal of Economic Theory, Elsevier, vol. 19(1), pages 150-162, October.
  9. 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.
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  11. Hadri, Kaddour, 1999. "Estimation of a Doubly Heteroscedastic Stochastic Frontier Cost Function," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 359-63, July.
  12. Konstantinos Giannakas & Kien C. Tran & Vangelis Tzouvelekas, 2003. "On the choice of functional form in stochastic frontier modeling," Empirical Economics, Springer, vol. 28(1), pages 75-100, January.
  13. 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.
  14. Berndt, Ernst R & Khaled, Mohammed S, 1979. "Parametric Productivity Measurement and Choice among Flexible Functional Forms," Journal of Political Economy, University of Chicago Press, vol. 87(6), pages 1220-45, December.
  15. Caudill, Steven B & Ford, Jon M & Gropper, Daniel M, 1995. "Frontier Estimation and Firm-Specific Inefficiency Measures in the Presence of Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 105-11, January.
  16. Olson, Jerome A. & Schmidt, Peter & Waldman, Donald M., 1980. "A Monte Carlo study of estimators of stochastic frontier production functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 67-82, May.
  17. Gong, Byeong-Ho & Sickles, Robin C., 1992. "Finite sample evidence on the performance of stochastic frontiers and data envelopment analysis using panel data," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 259-284.
  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. William C. Horrace & Peter Schmidt, 2002. "Confidence Statements for Efficiency Estimates from Stochastic Frontier Models," Econometrics 0206006, EconWPA.
  20. Kumbhakar, Subal C. & Löthgren, Mickael, 1998. "A Monte Carlo Analysis of Technical Inefficiency Predictors," Working Paper Series in Economics and Finance 229, Stockholm School of Economics.
  21. 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.
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Cited by:
  1. Ravallion, Martin, 2003. "In measuring aggregate"social efficiency"," Policy Research Working Paper Series 3166, The World Bank.
  2. repec:ise:isegwp:wp292008 is not listed on IDEAS

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