On Modeling and Interpreting the Economics of Catastrophic Climate Change
With climate change as prototype example, this paper analyzes the implications of structural uncertainty for the economics of low-probability, high-impact catastrophes. Even when updated by Bayesian learning, uncertain structural parameters induce a critical â€œtail fatteningâ€ of posterior-predictive distributions. Such fattened tails have strong implications for situations, like climate change, where a catastrophe is theoretically possible because prior knowledge cannot place sufficiently narrow bounds on overall damages. This paper shows that the economic consequences of fat-tailed structural uncertainty (along with unsureness about high-temperature damages) can readily outweigh the effects of discounting in climate-change policy analysis.
|Date of creation:||2009|
|Publication status:||Published in Review of Economics and Statistics|
|Contact details of provider:|| Postal: Littauer Center, Cambridge, MA 02138|
Web page: http://www.economics.harvard.edu/
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