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General Bayesian Learning in Dynamic Stochastic Models: Estimating the Value of Science Policy

Author

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  • Ivan Rudik

    (Cornell University)

  • Derek Lemoine

    (University of Arizona)

  • Maxwell Rosenthal

    (University of Arizona)

Abstract

We integrate climate scientists into an economic model of climate change by calibrating a statistical model for updating beliefs about the climate's sensitivity to greenhouse gas emissions to the actual history of scientific progress. We find that nonconjugate priors are critical for representing the observed dynamics of scientific knowledge. In order to investigate the implications for policy, we extend recursive dynamic programming methods to allow for nonconjugate learning about an uncertain parameter. We find that today's policymaker must set emission policy without the expectation that new information will enable timely revisions to policy. Improving scientific monitoring and climate modeling to enable faster learning would be worth up to \$XX dollars.

Suggested Citation

  • Ivan Rudik & Derek Lemoine & Maxwell Rosenthal, 2018. "General Bayesian Learning in Dynamic Stochastic Models: Estimating the Value of Science Policy," 2018 Meeting Papers 369, Society for Economic Dynamics.
    • Handle: RePEc:red:sed018:369
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    References listed on IDEAS

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