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

Richard Tol

Computational Economics, 2015, vol. 46, issue 2, 287-303

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 $$^{\circ }\hbox {C}$$ ∘ 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 $$2.5\,^{\circ }\hbox {C}$$ 2.5 ∘ C , rising to 15 % (50 %) for $$5.0\,^{\circ }\hbox {C}$$ 5.0 ∘ C for a rate of risk aversion of 1 (2). Copyright Springer Science+Business Media New York 2015

Keywords: Impacts of climate change; Kernel regression; Bootstrap; Risk aversion; Ambiguity aversion; C14; Q54 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (5)

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Working Paper: Bootstraps for Meta-Analysis with an Application to the Impact of Climate Change (2013)
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DOI: 10.1007/s10614-014-9448-5

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