Calibration of agricultural risk programming models using positive mathematical programming

Xuan Liu, Gerrit van Kooten and Jun Duan

Australian Journal of Agricultural and Resource Economics, 2020, vol. 64, issue 3

Abstract: Mathematical programming models of farmers’ cropping decisions must first be calibrated before they can be used to examine agricultural producer responses to policy changes. In this paper, we compare three calibration approaches for disentangling the risk parameter from the parameters of the cost function: one assumes a logarithmic utility function, while the others employ an exponential utility function. Historical crop insurance data for southern Alberta, Canada, are used to assess the calibration performance of the three approaches, and sensitivity analysis is implemented to test whether the changes in the optimal land allocation caused by the changes in the values of the parameters are practically reasonable. Only one of the three models is of practical use for policy analysis because it can recover the true values of the parameters and the results of sensitivity analysis are reasonable.

Keywords: Risk; and; Uncertainty (search for similar items in EconPapers)
Date: 2020
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Related works:
Journal Article: Calibration of agricultural risk programming models using positive mathematical programming (2020)
Working Paper: Calibration of Agricultural Risk Programming Models Using Positive Mathematical Programming (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:aareaj:338498

DOI: 10.22004/ag.econ.338498

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