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Positive Mathematical Programming with Generalized Risk

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  • Paris, Quirino

Abstract

Price risk in a mathematical programming framework has been confined for a long time to a constant risk aversion specification originally introduced by Freund in 1956. This paper extends the treatment of risk in a mathematical programming framework along the lines suggested by Meyer (1987) who demonstrated the equivalence of expected utility and a wide class of probability distributions that differ only by location and scale. This paper shows how to formulate a PMP specification that allows the estimation of the preference parameters and calibrates the model to the base data within an admissible small deviation. The PMP approach under generalized risk allows also the estimation of output supply elasticities. The approach is applied to a sample of large farms.

Suggested Citation

  • Paris, Quirino, 2014. "Positive Mathematical Programming with Generalized Risk," Working Papers 181605, University of California, Davis, Department of Agricultural and Resource Economics.
    • Handle: RePEc:ags:ucdavw:181605
    DOI: 10.22004/ag.econ.181605
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    References listed on IDEAS

    as
    1. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    2. Saha, Atanu, 1997. "Risk Preference Estimation in the Nonlinear Mean Standard Deviation Approach," Economic Inquiry, Western Economic Association International, vol. 35(4), pages 770-782, October.
    3. Meyer, Jack, 1987. "Two-moment Decision Models and Expected Utility Maximization," American Economic Review, American Economic Association, vol. 77(3), pages 421-430, June.
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    Keywords

    Productivity Analysis; Research Methods/ Statistical Methods; Risk and Uncertainty;
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