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Forecast Accuracy Matters for Hurricane Damage

Office of Macroeconomic Analysis, US Department of the Treasury, Washington, DC 20220, USA
Research Program on Forecasting, The George Washington University, Washington, DC 20052, USA
Climate Econometrics, Nuffield College, Oxford OX1 1NF, UK
Econometrics 2020, 8(2), 18;
Received: 17 February 2020 / Revised: 6 May 2020 / Accepted: 6 May 2020 / Published: 14 May 2020
(This article belongs to the Collection 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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MDPI and ACS Style

Martinez, A.B. Forecast Accuracy Matters for Hurricane Damage. Econometrics 2020, 8, 18.

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Martinez AB. Forecast Accuracy Matters for Hurricane Damage. Econometrics. 2020; 8(2):18.

Chicago/Turabian Style

Martinez, Andrew B. 2020. "Forecast Accuracy Matters for Hurricane Damage" Econometrics 8, no. 2: 18.

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