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Production under uncertainty: a simulation study

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  • Sriram Shankar
  • John Quiggin

Abstract

In this paper we model production technology in a state-contingent framework. Our model analyzes production under uncertainty without being explicit about the nature of producer risk preferences. In our model producers’ risk preferences are captured by the risk-neutral probabilities they assign to the different states of nature. Using a state-general state-contingent specification of technology we show that rational producers who encounter the same stochastic technology can make significantly different production choices. Further, we develop an econometric methodology to estimate the risk-neutral probabilities and the parameters of stochastic technology when there are two states of nature and only one of which is observed. Finally, we simulate data based on our state-general state-contingent specification of technology. Biased estimates of the technology parameters are obtained when we apply conventional ordinary least squares estimator on the simulated data. Copyright Springer Science+Business Media, LLC 2013

Suggested Citation

  • Sriram Shankar & John Quiggin, 2013. "Production under uncertainty: a simulation study," Journal of Productivity Analysis, Springer, vol. 39(3), pages 207-215, June.
    • Handle: RePEc:kap:jproda:v:39:y:2013:i:3:p:207-215
    DOI: 10.1007/s11123-012-0281-3
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    1. Powell, Alan A. & Gruen, Fred H.G., 1967. "The Estimation Of Production Frontiers: The Australian Livestock/Cereals Complex," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 11(1), pages 1-19, June.
    2. Christopher O’Donnell & Robert Chambers & John Quiggin, 2010. "Efficiency analysis in the presence of uncertainty," Journal of Productivity Analysis, Springer, vol. 33(1), pages 1-17, February.
    3. O'Donnell, Christopher J. & Shankar, Sriram, 2009. "Estimating State-Allocable Production Technologies When There are Two States of Nature and State Allocations of Inputs are Unobserved," 2009 Conference (53rd), February 11-13, 2009, Cairns, Australia 50898, Australian Agricultural and Resource Economics Society.
    4. C. J. O'Donnell & W. E. Griffiths, 2006. "Estimating State-Contingent Production Frontiers," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(1), pages 249-266.
    5. Rasmussen, Svend, 2003. "Criteria for optimal production under uncertainty. The state-contingent approach," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 47(4), pages 1-30.
    6. Jean-Paul Chavas, 2008. "On the economics of agricultural production ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(4), pages 365-380, December.
    7. Jean-Paul Chavas, 2008. "A Cost Approach to Economic Analysis Under State-Contingent Production Uncertainty," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(2), pages 435-466.
    8. Chambers,Robert G. & Quiggin,John, 2000. "Uncertainty, Production, Choice, and Agency," Cambridge Books, Cambridge University Press, number 9780521622448, September.
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    Cited by:

    1. Shankar, Sriram, 2015. "Efficiency analysis under uncertainty: a simulation study," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 59(2), April.
    2. Robert Chambers & Teresa Serra & Spiro Stefanou, 2015. "Using ex ante output elicitation to model state-contingent technologies," Journal of Productivity Analysis, Springer, vol. 43(1), pages 75-83, February.
    3. Taylan G. Topcu & Konstantinos Triantis, 2022. "An ex-ante DEA method for representing contextual uncertainties and stakeholder risk preferences," Annals of Operations Research, Springer, vol. 309(1), pages 395-423, February.
    4. Kota Minegishi, 2016. "Comparison of production risks in the state-contingent framework: application to balanced panel data," Journal of Productivity Analysis, Springer, vol. 46(2), pages 121-138, December.
    5. Raushan Bokusheva & Lajos Baráth, 2024. "State‐contingent production technology formulation: Identifying states of nature using reduced‐form econometric models of crop yield," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(2), pages 805-827, March.

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    More about this item

    Keywords

    CES; Cobb–Douglas; Output-cubical; Risk-neutral; State-allocable; C63; D21; D22; D81; Q10;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q10 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - General

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