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- W3024583013 abstract "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." @default.
- W3024583013 created "2020-05-21" @default.
- W3024583013 creator A5048651795 @default.
- W3024583013 date "2020-05-14" @default.
- W3024583013 modified "2023-09-25" @default.
- W3024583013 title "Forecast Accuracy Matters for Hurricane Damage" @default.
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- W3024583013 doi "https://doi.org/10.3390/econometrics8020018" @default.
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