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Measuring Technical Efficiency in the Stochastic Varying Coefficient Frontier Model


  • Giannis Karagiannis
  • Vangelis Tzouvelekas

    () (Department of Economics, University of Crete, Greece)


Due to the assumption that the best practice methods refer to each input separately instead of the whole set of inputs used by a firm, the benchmark technology as defined in the stochastic varying coefficient frontier model may be infeasible and theoretically improper whenever the maximum response coefficients are not coming from the same production unit. To overcome this problem, we propose alternative measures of output-oriented and single-factor technical efficiencies inspired from the maximum likelihood formulation of the nonneutral frontier model. The empirical results indicate that there are significant differences between the two in terms of the estimated efficiency scores but not significant differences we detected in terms of the efficiency ranking. Copyright (c) 2009 International Association of Agricultural Economists.
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  • Giannis Karagiannis & Vangelis Tzouvelekas, 2007. "Measuring Technical Efficiency in the Stochastic Varying Coefficient Frontier Model," Working Papers 0725, University of Crete, Department of Economics.
  • Handle: RePEc:crt:wpaper:0725

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    References listed on IDEAS

    1. Efthymios G. Tsionas, 2002. "Stochastic frontier models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 127-147.
    2. Raymond J. Kopp, 1981. "The Measurement of Productive Efficiency: A Reconsideration," The Quarterly Journal of Economics, Oxford University Press, vol. 96(3), pages 477-503.
    3. Tim Coelli & Sergio Perelman & Elliot Romano, 1999. "Accounting for Environmental Influences in Stochastic Frontier Models: With Application to International Airlines," Journal of Productivity Analysis, Springer, vol. 11(3), pages 251-273, June.
    4. K.P. Kalirajan & M.B. Obwona & S. Zhao, 1996. "A Decomposition of Total Factor Productivity Growth: The Case of Chinese Agricultural Growth before and after Reforms," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(2), pages 331-338.
    5. 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.
    6. 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.
    7. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
    8. Kodde, David A & Palm, Franz C, 1986. "Wald Criteria for Jointly Testing Equality and Inequality Restriction s," Econometrica, Econometric Society, vol. 54(5), pages 1243-1248, September.
    9. Stijn Reinhard & C.A. Knox Lovell & Geert Thijssen, 1999. "Econometric Estimation of Technical and Environmental Efficiency: An Application to Dutch Dairy Farms," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(1), pages 44-60.
    10. K. P. Kalirajan & Yiping Huang, 2001. "Does China Have a Grain Problem? An Empirical Analysis," Oxford Development Studies, Taylor & Francis Journals, vol. 29(1), pages 45-55.
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    Cited by:

    1. Pede, Valerien O. & McKinley, Justin & Singbo, Alphonse & Kajisa, Kei, 2015. "Spatial Dependency of Technical Efficiency in Rice Farming: The Case of Bohol, Philippines," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205456, Agricultural and Applied Economics Association;Western Agricultural Economics Association.
    2. A. Peyrache & A. N. Rambaldi, 2017. "Incorporating temporal and country heterogeneity in growth accounting—an application to EU-KLEMS," Journal of Productivity Analysis, Springer, vol. 47(2), pages 143-166, April.
    3. Singbo, Alphonse G. & Emvalomatis, Grigorios & Alfons, Oude Lansink, 2013. "Assessing the impact of crop specialization on farms’ performance in vegetables farming in Benin: a non-neutral stochastic frontier approach," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149172, Agricultural and Applied Economics Association.
    4. Tankari, Mahamadou Roufahi, 2014. "Quelles zones cibler pour accroître l’efficacité agricole en Ouganda?," MPRA Paper 53396, University Library of Munich, Germany.
    5. Dios-Palomares, Rafaela & Martínez-Paz, José M., 2011. "Technical, quality and environmental efficiency of the olive oil industry," Food Policy, Elsevier, vol. 36(4), pages 526-534, August.

    More about this item


    stochastic varying coefficient frontier model; input specific technical efficiency; olive farming; Greece;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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