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Economic policy when models disagree

  • Pauline Barrieu
  • Sinclair Desgagn�
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    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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    Paper provided by Grantham Research Institute on Climate Change and the Environment in its series GRI Working Papers with number 4.

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    Date of creation: Jul 2009
    Date of revision:
    Handle: RePEc:lsg:lsgwps:wp04
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    1. 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.
    2. 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.
    3. 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.
    4. Richard Clarida & Jordi Gali & Mark Gertler, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," NBER Working Papers 7147, National Bureau of Economic Research, Inc.
    5. Rama Cont, 2006. "Model Uncertainty And Its Impact On The Pricing Of Derivative Instruments," Mathematical Finance, Wiley Blackwell, vol. 16(3), pages 519-547.
    6. Acocella, Nicola & Di Bartolomeo, Giovanni, 2007. "Towards a new theory of economic policy: Continuity and innovation," MPRA Paper 4419, University Library of Munich, Germany.
    7. Thibault Gadjos & Jean-Marc Tallon & Jean-Christophe Vergnaud, 2002. "Decision Making with Imprecise Probabilistic Information," Working Papers 2002-33, Centre de Recherche en Economie et Statistique.
    8. Catarina Roseta-Palma & Anastasios Xepapadeas, 2004. "Robust Control in Water Management," Journal of Risk and Uncertainty, Springer, vol. 29(1), pages 21-34, 07.
    9. 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.
    10. Antoine Billot & Itzhak Gilboa & David Schmeidler & Dov Samet, 2004. "Probabilities as Similarity-Weighted Frequencies," Cowles Foundation Discussion Papers 1492, Cowles Foundation for Research in Economics, Yale University.
    11. 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.
    12. 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.
    13. 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.
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