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Open AccessArticle

Forecast Accuracy Matters for Hurricane Damage

1
Office of Macroeconomic Analysis, US Department of the Treasury, Washington, DC 20220, USA
2
Research Program on Forecasting, The George Washington University, Washington, DC 20052, USA
3
Climate Econometrics, Nuffield College, Oxford OX1 1NF, UK
Econometrics 2020, 8(2), 18; https://doi.org/10.3390/econometrics8020018
Received: 17 February 2020 / Revised: 6 May 2020 / Accepted: 6 May 2020 / Published: 14 May 2020
(This article belongs to the Special Issue Econometric Analysis of Climate Change)
I analyze damage from hurricane strikes on the United States since 1955. Using machine learning methods to select the most important drivers for damage, I show that large errors in a hurricane’s predicted landfall location result in higher damage. 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 damage 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. View Full-Text
Keywords: adaptation; model selection; natural disasters; uncertainty adaptation; model selection; natural disasters; uncertainty
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Martinez, A.B. Forecast Accuracy Matters for Hurricane Damage. Econometrics 2020, 8, 18.

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