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Calibration of agricultural risk programming models.

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Title: Calibration of agricultural risk programming models.
Authors: Petsakos, Athanasios1 apetsakos@grignon.inra.fr, Rozakis, Stelios2 s.rozakis@aua.gr
Source: European Journal of Operational Research. Apr2015, Vol. 242 Issue 2, p536-545. 10p.
Subjects: Agricultural industries, Agriculture, Risk assessment, Mathematical programming, Decision making, Scientific observation, Agricultural economics
Abstract: Positive Mathematical Programming (PMP) is one of the most commonly used methods for calibrating activity programming models. In this article we consider PMP as a calibration method for risk programming models with a mean-variance (E-V) specification. We argue that the restrictive theoretical assumptions employed by typical linear E-V models limit their applicability in analyzing the effects of decoupled payments on agricultural production decisions. Furthermore, the requirement for eliciting a risk aversion coefficient renders such models incompatible with the PMP method. For this reason we propose a nonlinear E-V specification and develop a PMP-based procedure for its calibration which does not aim at introducing (further) nonlinearities in the objective function, but at recovering the “true” distribution of wealth that will allow the final model to reproduce base year observations. We also examine how our approach relates to the recent PMP developments on calibration against elasticity priors and we show how such priors can be used for the calibration of the nonlinear E-V model. [ABSTRACT FROM AUTHOR]
Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 100155911
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PubType: Academic Journal
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  Data: Calibration of agricultural risk programming models.
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  Data: <searchLink fieldCode="AR" term="%22Petsakos%2C+Athanasios%22">Petsakos, Athanasios</searchLink><relatesTo>1</relatesTo><i> apetsakos@grignon.inra.fr</i><br /><searchLink fieldCode="AR" term="%22Rozakis%2C+Stelios%22">Rozakis, Stelios</searchLink><relatesTo>2</relatesTo><i> s.rozakis@aua.gr</i>
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Operational+Research%22">European Journal of Operational Research</searchLink>. Apr2015, Vol. 242 Issue 2, p536-545. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Agricultural+industries%22">Agricultural industries</searchLink><br /><searchLink fieldCode="DE" term="%22Agriculture%22">Agriculture</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+programming%22">Mathematical programming</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+observation%22">Scientific observation</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+economics%22">Agricultural economics</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Positive Mathematical Programming (PMP) is one of the most commonly used methods for calibrating activity programming models. In this article we consider PMP as a calibration method for risk programming models with a mean-variance (E-V) specification. We argue that the restrictive theoretical assumptions employed by typical linear E-V models limit their applicability in analyzing the effects of decoupled payments on agricultural production decisions. Furthermore, the requirement for eliciting a risk aversion coefficient renders such models incompatible with the PMP method. For this reason we propose a nonlinear E-V specification and develop a PMP-based procedure for its calibration which does not aim at introducing (further) nonlinearities in the objective function, but at recovering the “true” distribution of wealth that will allow the final model to reproduce base year observations. We also examine how our approach relates to the recent PMP developments on calibration against elasticity priors and we show how such priors can be used for the calibration of the nonlinear E-V model. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.ejor.2014.10.018
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 536
    Subjects:
      – SubjectFull: Agricultural industries
        Type: general
      – SubjectFull: Agriculture
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Mathematical programming
        Type: general
      – SubjectFull: Decision making
        Type: general
      – SubjectFull: Scientific observation
        Type: general
      – SubjectFull: Agricultural economics
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      – TitleFull: Calibration of agricultural risk programming models.
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              Text: Apr2015
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              Y: 2015
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