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Specification and estimation of primal production models

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  • Kumbhakar, Subal C.

Abstract

While estimating production technology in a primal framework production function, input and output distance functions and input requirement functions are widely used in the empirical literature. This paper shows that these popular primal based models are algebraically equivalent in the sense that they can be derived from the same underlying transformation (production possibility) function. By assuming that producers maximize profit, we show that in all cases, except one, the use of ordinary least squares (OLS) gives inconsistent estimates irrespective of whether the production, input distance and input requirement functions are used. Based on several specifications of the production and input distance function models, we conclude that one can estimate the input elasticities and returns to scale consistently using instruments on only one regressor. No instruments are needed if either it is assumed that producers know the technology entirely (including the so-called error term) or a system approach is used. We used Norwegian timber harvesting data to illustrate workings of various model specifications.

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Bibliographic Info

Article provided by Elsevier in its journal European Journal of Operational Research.

Volume (Year): 217 (2012)
Issue (Month): 3 ()
Pages: 509-518

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Handle: RePEc:eee:ejores:v:217:y:2012:i:3:p:509-518

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Web page: http://www.elsevier.com/locate/eor

Related research

Keywords: Production function; Input distance function; Input requirement function; Cobb–Douglas; Translog;

References

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  1. Kumbhakar, Subal C., 2011. "Estimation of production technology when the objective is to maximize return to the outlay," European Journal of Operational Research, Elsevier, vol. 208(2), pages 170-176, January.
  2. Fare, Rolf & Grosskopf, Shawna & Zaim, Osman, 2002. "Hyperbolic efficiency and return to the dollar," European Journal of Operational Research, Elsevier, vol. 136(3), pages 671-679, February.
  3. Perelman, Sergio & Santín, Daniel, 2009. "How to generate regularly behaved production data? A Monte Carlo experimentation on DEA scale efficiency measurement," European Journal of Operational Research, Elsevier, vol. 199(1), pages 303-310, November.
  4. Subal C. Kumbhakar & Efthymios G. Tsionas, 2011. "Stochastic error specification in primal and dual production systems," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(2), pages 270-297, March.
  5. COELLI, Tim, 2000. "On the econometric estimation of the distance function representation of a production technology," CORE Discussion Papers 2000042, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  6. James Levinsohn & Amil Petrin, 2000. "Estimating Production Functions Using Inputs to Control for Unobservables," NBER Working Papers 7819, National Bureau of Economic Research, Inc.
  7. Diewert, W E, 1974. "Functional Forms for Revenue and Factor Requirements Functions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(1), pages 119-30, February.
  8. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," Review of Economic Studies, Oxford University Press, vol. 70(2), pages 317-341.
  9. repec:fth:louvco:0042 is not listed on IDEAS
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Cited by:
  1. Kumbhakar, Subal C., 2013. "Specification and estimation of multiple output technologies: A primal approach," European Journal of Operational Research, Elsevier, vol. 231(2), pages 465-473.

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