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Forecast Accuracy Matters for Hurricane Damages

Andrew Martinez ()

No 2020-003, Working Papers from The George Washington University, Department of Economics, Research Program on Forecasting

Abstract: I analyze damages from hurricane strikes on the United States since 1955. Using machine learning methods to select the most important drivers for damages, I show that large errors in a hurricane’s predicted landfall location result in higher damages. This relationship holds across a wide range of model specifications and when controlling for ex-ante uncertainty and potential endogeneity. Using a counterfactual exercise I find that the cumulative reduction in damages from forecast improvements since 1970 is about $82 billion, which exceeds the U.S. government’s spending on the forecasts and private willingness to pay for them.

Keywords: Adaptation; Model Selection; Natural Disasters; Uncertainty (search for similar items in EconPapers)
JEL-codes: C51 C52 Q51 Q54 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2020-05
New Economics Papers: this item is included in nep-ure
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https://www2.gwu.edu/~forcpgm/2020-003.pdf First version, 2020 (application/pdf)

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