Artikel
Forecast accuracy matters for hurricane damage
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.
- Language
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Englisch
- Bibliographic citation
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 8 ; Year: 2020 ; Issue: 2 ; Pages: 1-24 ; Basel: MDPI
- Classification
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Wirtschaft
- Subject
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adaptation
model selection
natural disasters
uncertainty
- Event
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Geistige Schöpfung
- (who)
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Martinez, Andrew B.
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2020
- DOI
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doi:10.3390/econometrics8020018
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Martinez, Andrew B.
- MDPI
Time of origin
- 2020
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