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Production Risk and the Estimation of Ex Ante Cost Functions

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Abstract

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 can be 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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  • GianCarlo Moschini, 2000. "Production Risk and the Estimation of Ex Ante Cost Functions," Center for Agricultural and Rural Development (CARD) Publications 00-wp262, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    • Handle: RePEc:ias:cpaper:00-wp262
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    2. Cherchye, L. & Post, G.T., 2001. "Methodological Advances in Dea," ERIM Report Series Research in Management ERS-2001-53-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    3. Just, David R., 2011. "Calibrating the wealth effects of decoupled payments: Does decreasing absolute risk aversion matter?," Journal of Econometrics, Elsevier, vol. 162(1), pages 25-34, May.
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    5. Just, Richard E., 2000. "Some Guiding Principles for Empirical Production Research in Agriculture," Agricultural and Resource Economics Review, Cambridge University Press, vol. 29(2), pages 138-158, October.
    6. Philippe K. Widmer & Maria Trottmann & Peter Zweifel, 2018. "Choice of reserve capacity by hospitals: a problem for prospective payment," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(5), pages 663-673, June.
    7. Jesse B. Tack & Rulon D. Pope & Jeffrey T. LaFrance & Ricardo H. Cavazos, 2015. "Modelling an aggregate agricultural panel with application to US farm input demands," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 42(3), pages 371-396.
    8. Just, Richard E., 2003. "Risk research in agricultural economics: opportunities and challenges for the next twenty-five years," Agricultural Systems, Elsevier, vol. 75(2-3), pages 123-159.
    9. Alston, Julian M. & Andersen, Matthew A. & Pardey, Philip G., 2006. "Asset Utilization and Bias in Measures of U.S. Agricultural Productivity," 2006 Annual meeting, July 23-26, Long Beach, CA 21220, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
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    13. Hansen, Kristiana & Frahan, Bruno Henry de, 2011. "Evaluation of Agro-Environmental Policy through a Calibrated Simulation Farm Model," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114577, European Association of Agricultural Economists.
    14. Esposti, Roberto & Pierani, Pierpaolo, 2002. "Public R&D Investment and Cost Behaviour in Italian Agriculture: 1960-1995," 2002 International Congress, August 28-31, 2002, Zaragoza, Spain 24974, European Association of Agricultural Economists.
    15. Bittencourt, Mauricio Vaz Lobo & Sampaio, Armando Vaz, 2011. "Are Dual and Primal Estimations Equivalent in the Presence of Stochastic Errors in Input Demand?," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(2), December.
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