Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/247566 
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Erscheinungsjahr: 
2020
Quellenangabe: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 8 [Issue:] 2 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-24
Verlag: 
MDPI, Basel
Zusammenfassung: 
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.
Schlagwörter: 
adaptation
model selection
natural disasters
uncertainty
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