Forecast Accuracy Matters for Hurricane Damages
Andrew Martinez ()
No 2020-003, Working Papers from The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting
I analyze damages from hurricane strikes on the United States since 1955. Using machine learning methods to select the most important drivers for damages, I show that large errors in a hurricane’s predicted landfall location result in higher damages. 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 damages 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: C51 C52 Q51 Q54 (search for similar items in EconPapers)
Pages: 35 pages
New Economics Papers: this item is included in nep-big, nep-env and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4) Track citations by RSS feed
Downloads: (external link)
https://www2.gwu.edu/~forcpgm/2020-003.pdf First version, 2020 (application/pdf)
Journal Article: Forecast Accuracy Matters for Hurricane Damage (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:gwc:wpaper:2020-003
Access Statistics for this paper
More papers in Working Papers from The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting Contact information at EDIRC.
Bibliographic data for series maintained by GW Economics Department ().