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Integrating risk and uncertainty in PMP models

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  • Petsakos, Athanasios
  • Rozakis, Stelios

Abstract

Positive Mathematical Programming (PMP) is one of the most commonly used methods of calibrating activity linear programming (LP) models in agriculture. PMP applications published thus far focus on the estimation of a farm’s nonlinear cost or profit function and rely on the recovery of unobserved or implicit information that can explain the initial model’s inability to calibrate. In this paper we use the PMP procedure to calibrate an expected utility model under the assumption that this implicit information can reveal a farmer’s profit expectations and risk attitude. The perfect calibration shows that PMP can be applied not only to LP models, but also to models that incorporate risk and this provides an interesting alternative to the traditional PMP methodology.

Suggested Citation

  • Petsakos, Athanasios & Rozakis, Stelios, 2011. "Integrating risk and uncertainty in PMP models," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114762, European Association of Agricultural Economists.
    • Handle: RePEc:ags:eaae11:114762
    DOI: 10.22004/ag.econ.114762
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    Cited by:

    1. Xuan Liu & Jun Duan & G. Cornelis van Kooten, 2018. "The impact of changes in the AgriStability program on crop activities: A farm modeling approach," Agribusiness, John Wiley & Sons, Ltd., vol. 34(3), pages 650-667, June.
    2. Xuan Liu & Jun Duan & G. Cornelis van Kooten, 2015. "An Evaluation of the Effects of Changes in the AgriStability Program on Producers’ Crop Activities: A Farm Modeling Approach," Working Papers 2015-02, University of Victoria, Department of Economics, Resource Economics and Policy Analysis Research Group.
    3. Esther Boere & G. Cornelis van Kooten, 2015. "Reforming the Common Agricultural Policy: Decoupling Agricultural Payments from Production and Promoting the Environment," Working Papers 2015-01, University of Victoria, Department of Economics, Resource Economics and Policy Analysis Research Group.
    4. Jansson, Torbjörn & Heckelei, Thomas & Gocht, Alexander & Basnet, Shyam Kumar & Zhang, Yinan & Neuenfeldt, Sebastian, 2014. "Analysing impacts of changing price variability with estimated farm risk-programming models," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182665, European Association of Agricultural Economists.
    5. Liu, Xuan & van Kooten, Gerrit Cornelis & Duan, Jun, 2020. "Calibration of agricultural risk programming models using positive mathematical programming," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(3), July.
    6. Christina Moulogianni, 2022. "Comparison of Selected Mathematical Programming Models Used for Sustainable Land and Farm Management," Land, MDPI, vol. 11(8), pages 1-18, August.
    7. Heckelei, Thomas & Britz, Wolfgang & Zhang, Yinan, 2012. "Positive Mathematical Programming Approaches – Recent Developments in Literature and Applied Modelling," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 1(1), pages 1-16, April.
    8. Arata, Linda & Donati, Michele & Sckokai, Paolo & Arfini, Filippo, 2014. "Incorporating risk in a positive mathematical programming framework: a new methodological approach," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182659, European Association of Agricultural Economists.

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    Risk and Uncertainty;

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