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Estimating Variable Returns to Scale Production Frontiers with Alternative Stochastic Assumptions

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  • William E. Griffiths
  • Christopher J. O’Donnell

Abstract

A stochastic production frontier model is formulated within the generalized production function framework popularized by Zellner and Revankar (1969) and Zellner and Ryu (1998). This framework is convenient for parsimonious modeling of a production function with variable returns to scale specified as a function of output. Two alternatives for introducing the stochastic inefficiency term and the stochastic error are considered, one where they are appended to the existing equation for the production relationship and one where the existing equation is solved for the log of output before the stochastic terms are added. The latter alternative is novel, but it is needed to preserve the usual definition of firm efficiency. The two alternative stochastic assumptions are considered in conjunction with two returns to scale functions, making a total of four models that are considered. A Bayesian framework for estimating all four models is described. The techniques are applied to USDA state-level data on agricultural output and four inputs. Posterior distributions for all parameters, firm efficiencies and the efficiency rankings of firms are obtained. The sensitivity of the results to the returns to scale specification and to the stochastic specification is examined.

Suggested Citation

  • William E. Griffiths & Christopher J. O’Donnell, 2003. "Estimating Variable Returns to Scale Production Frontiers with Alternative Stochastic Assumptions," Department of Economics - Working Papers Series 872, The University of Melbourne.
    • Handle: RePEc:mlb:wpaper:872
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    1. Scott Atkinson & Jeffrey Dorfman, 2005. "Multiple Comparisons with the Best: Bayesian Precision Measures of Efficiency Rankings," Journal of Productivity Analysis, Springer, vol. 23(3), pages 359-382, July.
    2. Arnold Zellner & Hang Ryu, 1998. "Alternative functional forms for production, cost and returns to scale functions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(2), pages 101-127.
    3. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    4. Revankar, Nagesh S, 1971. "A Class of Variable Elasticity of Substitution Production Functions," Econometrica, Econometric Society, vol. 39(1), pages 61-71, January.
    5. A. Zellner & N. S. Revankar, 1969. "Generalized Production Functions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 36(2), pages 241-250.
    6. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
    7. Koop, Gary & Steel, Mark F.J. & Osiewalski, Jacek, 1992. "Posterior analysis of stochastic frontier models using Gibbs sampling," DES - Working Papers. Statistics and Econometrics. WS 3677, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Fernandez, Carmen & Osiewalski, Jacek & Steel, Mark F. J., 1997. "On the use of panel data in stochastic frontier models with improper priors," Journal of Econometrics, Elsevier, vol. 79(1), pages 169-193, July.
    9. John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
    10. Kumbhakar, Subal C & Ghosh, Soumendra & McGuckin, J Thomas, 1991. "A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in U.S. Dairy Farms," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(3), pages 279-286, July.
    11. V. Eldon Ball & Frank M. Gollop & Alison Kelly-Hawke & Gregory P. Swinand, 1999. "Patterns of State Productivity Growth in the U.S. Farm Sector: Linking State and Aggregate Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(1), pages 164-179.
    12. Ball, Eldon & Nehring, Richard, 1998. "Patterns of State Productivity Growth in the U.S. Farm Sector," Staff Reports 278831, United States Department of Agriculture, Economic Research Service.
    13. 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 727-743, November.
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    Cited by:

    1. Glass, Anthony J. & Kenjegalieva, Karligash & Sickles, Robin C. & Weyman-Jones, Thomas, 2016. "The Spatial Efficiency Multiplier and Random Effects in Spatial Stochastic Frontier Models," Working Papers 16-002, Rice University, Department of Economics.
    2. Ipatova, Irina & Peresetsky, Аnatoly, 2013. "Technical efficiency of Russian plastic and rubber production firms," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 32(4), pages 71-92.

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