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Economic policy when models disagree

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  • Pauline Barrieu
  • Sinclair Desgagné
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    Abstract

    This paper proposes a general way to conceive public policy when there is noconsensual account of the situation of interest. The approach builds on an extension and dual formulation of the traditional theory of economic policy. It does not need a representative policymaker’s utility function (as in the literature on ambiguity), a reference model (as in robust control theory) or some prior probability distribution over the set of supplied scenarios (as in Bayesian model-averaging). The method requires instead that the willingness to accept a policy’s projected outcomes coincide with the willingness to pay to correct the current situation. Policies constructed in this manner are shown to be e ective, robust and simple in a precise and intuitive sense.

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    Bibliographic Info

    Paper provided by Grantham Research Institute on Climate Change and the Environment in its series Grantham Research Institute on Climate Change and the Environment Working Papers with number 4.

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    Date of creation: Jul 2009
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    Handle: RePEc:lsg:lsgwps:wp04

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    1. Fabio Maccheroni & Massimo Marinacci & Aldo Rustichini, 2004. "Ambiguity Aversion, Robustness, and the Variational Representation of Preferences," Carlo Alberto Notebooks 12, Collegio Carlo Alberto, revised 2006.
    2. Rama Cont, 2006. "Model Uncertainty And Its Impact On The Pricing Of Derivative Instruments," Mathematical Finance, Wiley Blackwell, vol. 16(3), pages 519-547.
    3. Acocella Nicola & Di Bartolomeo Giovanni, 2007. "Towards a new theory of economic policy: Continuity and innovation," wp.comunite 0020, Department of Communication, University of Teramo.
    4. John C. Williams & Andrew T. Levin, 2003. "Robust Monetary Policy with Competing Reference Models," Computing in Economics and Finance 2003 291, Society for Computational Economics.
    5. Thibault Gajdos & Jean-Marc Tallon & Jean-Christophe Vergnaud, 2002. "Decision Making with Imprecise Probabilistic Information," ICER Working Papers - Applied Mathematics Series 18-2003, ICER - International Centre for Economic Research, revised May 2003.
    6. Geoffrey Heal, 2009. "Climate Economics: A Meta-Review and Some Suggestions for Future Research," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 3(1), pages 4-21, Winter.
    7. Giannis Vardas & Anastasios Xepapadeas, 2010. "Model Uncertainty, Ambiguity and the Precautionary Principle: Implications for Biodiversity Management," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 45(3), pages 379-404, March.
    8. Antoine Billot & Itzhak Gilboa & Dov Samet & David Schmeidler, 2004. "Probabilities as Similarity-Weighted Frequencies," Levine's Bibliography 122247000000000696, UCLA Department of Economics.
    9. Acocella, N. & Di Bartolomeo, G., 2006. "Tinbergen and Theil meet Nash: Controllability in policy games," Economics Letters, Elsevier, vol. 90(2), pages 213-218, February.
    10. Dieter Helm, 2008. "Climate-change policy: why has so little been achieved?," Oxford Review of Economic Policy, Oxford University Press, vol. 24(2), pages 211-238, Summer.
    11. Manne, Alan & Mendelsohn, Robert & Richels, Richard, 1995. "MERGE : A model for evaluating regional and global effects of GHG reduction policies," Energy Policy, Elsevier, vol. 23(1), pages 17-34, January.
    12. Catarina Roseta-Palma & Anastasios Xepapadeas, 2004. "Robust Control in Water Management," Journal of Risk and Uncertainty, Springer, vol. 29(1), pages 21-34, 07.
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