Computational Economics

, Volume 46, Issue 2, pp 287–303 | Cite as

Bootstraps for Meta-Analysis with an Application to the Impact of Climate Change

Article

Abstract

Bootstrap and smoothed bootstrap methods are used to estimate the uncertainty about the total impact of climate change, and to assess the performance of commonly used impact functions. Kernel regression is extended to include restrictions on the functional form. Impact functions do not describe the primary estimates of the economic impacts very well, and monotonic functions do particularly badly. The impacts of climate change do not significantly deviate from zero until 2.5–3.5 \(^{\circ }\hbox {C}\) warming. The uncertainty is large, and so is the risk premium. The ambiguity premium is small, however. The certainty equivalent impact is a negative 1.5 % of income for \(2.5\,^{\circ }\hbox {C}\), rising to 15 % (50 %) for \(5.0\,^{\circ }\hbox {C}\) for a rate of risk aversion of 1 (2).

Keywords

Impacts of climate change Kernel regression Bootstrap  Risk aversion Ambiguity aversion 

JEL Classification

C14 Q54 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of EconomicsUniversity of SussexFalmerUK
  2. 2.Institute for Environmental StudiesVrije UniversiteitAmsterdamThe Netherlands
  3. 3.Department of Spatial EconomicsVrije UniversiteitAmsterdamThe Netherlands
  4. 4.Tinbergen InstituteAmsterdamThe Netherlands
  5. 5.CESifoMunichGermany

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