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

Ivan Rudik, Derek Lemoine and Maxwell Rosenthal
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Maxwell Rosenthal: University of Arizona

No 369, 2018 Meeting Papers from Society for Economic Dynamics

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.

Date: 2018
New Economics Papers: this item is included in nep-dge, nep-ene and nep-env
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Persistent link: https://EconPapers.repec.org/RePEc:red:sed018:369

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