Forecast Accuracy Matters for Hurricane Damage
Andrew Martinez ()
Econometrics, 2020, vol. 8, issue 2, 1-24
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.
Keywords: adaptation; model selection; natural disasters; uncertainty (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
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Working Paper: Forecast Accuracy Matters for Hurricane Damages (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:8:y:2020:i:2:p:18-:d:357835
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