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Production Risk and the Estimation of Ex Ante Cost Functions

  • Moschini, GianCarlo

Cost function estimation under production uncertainty is problematic because the relevant cost is conditional on unobservable expected output. If input demand functions are also stochastic, then a nonlinear errors-in-variables model is obtained and standard estimation procedures typically fail to attain consistency. But by exploiting the full implications of the expected profit maximization hypothesis that gives rise to ex-ante cost functions, it is shown that the errors-in-variables problem can be effectively removed, and consistent estimation of the parameters of interest achieved. A Monte Carlo experiment illustrates the advantages of the proposed procedure as well as the pitfalls of other existing estimators.

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File URL: http://www.econ.iastate.edu/sites/default/files/publications/papers/paper_1922.pdf
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Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number 1922.

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Date of creation: 01 Jan 2001
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Publication status: Published in Journal of Econometrics 2001, vol. 100, pp. 357-380
Handle: RePEc:isu:genres:1922
Contact details of provider: Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070
Phone: +1 515.294.6741
Fax: +1 515.294.0221
Web page: http://www.econ.iastate.edu
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  1. Rulon D. Pope & Richard E. Just, 1998. "Cost Function Estimation under Risk Aversion," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(2), pages 296-302.
  2. Berndt, Ernst R & Wood, David O, 1975. "Technology, Prices, and the Derived Demand for Energy," The Review of Economics and Statistics, MIT Press, vol. 57(3), pages 259-68, August.
  3. McFadden, Daniel, 1978. "Cost, Revenue, and Profit Functions," Histoy of Economic Thought Chapters, in: Fuss, Melvyn & McFadden, Daniel (ed.), Production Economics: A Dual Approach to Theory and Applications, volume 1, chapter 1 McMaster University Archive for the History of Economic Thought.
  4. J. A. Hausman & W. K. Newey & J. L. Powel, 1988. "Nonlinear Errors in Variables: Estimation of Some Engel Curves," Working papers 504, Massachusetts Institute of Technology (MIT), Department of Economics.
  5. Pope, Rulon D. & Just, Richard E., 1996. "Empirical implementation of ex ante cost functions," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 231-249.
  6. Amemiya, Yasuo, 1985. "Instrumental variable estimator for the nonlinear errors-in-variables model," Journal of Econometrics, Elsevier, vol. 28(3), pages 273-289, June.
  7. V. Eldon Ball & Jean-Christophe Bureau & Richard Nehring & Agapi Somwaru, 1997. "Agricultural Productivity Revisited," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(4), pages 1045-1063.
  8. Amemiya, Yasuo, 1990. "Two-stage instrumental variables estimators for the nonlinear errors-in-variables model," Journal of Econometrics, Elsevier, vol. 44(3), pages 311-332, June.
  9. Hsiao, C., 1988. "Consistent Estimation For Some Nonlinear Errors-In- Variables Models," Papers m8810, Southern California - Department of Economics.
  10. Robert G. Chambers & John Quiggin, 1998. "Cost Functions and Duality for Stochastic Technologies," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(2), pages 288-295.
  11. McElroy, Marjorie B, 1987. "Additive General Error Models for Production, Cost, and Derived Demand or Share Systems," Journal of Political Economy, University of Chicago Press, vol. 95(4), pages 737-57, August.
  12. Diewert, W E, 1971. "An Application of the Shephard Duality Theorem: A Generalized Leontief Production Function," Journal of Political Economy, University of Chicago Press, vol. 79(3), pages 481-507, May-June.
  13. Lau, Lawrence J., 1976. "A characterization of the normalized restricted profit function," Journal of Economic Theory, Elsevier, vol. 12(1), pages 131-163, February.
  14. Hausman, Jerry A. & Newey, Whitney K. & Ichimura, Hidehiko & Powell, James L., 1991. "Identification and estimation of polynomial errors-in-variables models," Journal of Econometrics, Elsevier, vol. 50(3), pages 273-295, December.
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