IDEAS home Printed from https://ideas.repec.org/a/gam/jecnmx/v8y2020i2p18-d357835.html
   My bibliography  Save this article

Forecast Accuracy Matters for Hurricane Damage

Author

Listed:
  • Andrew B. Martinez

    () (Office of Macroeconomic Analysis, US Department of the Treasury, Washington, DC 20220, USA
    Research Program on Forecasting, The George Washington University, Washington, DC 20052, USA
    Climate Econometrics, Nuffield College, Oxford OX1 1NF, UK)

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.

Suggested Citation

  • Andrew B. Martinez, 2020. "Forecast Accuracy Matters for Hurricane Damage," Econometrics, MDPI, Open Access Journal, vol. 8(2), pages 1-24, May.
  • Handle: RePEc:gam:jecnmx:v:8:y:2020:i:2:p:18-:d:357835
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2225-1146/8/2/18/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2225-1146/8/2/18/
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Felix Pretis & Lea Schneider & Jason E. Smerdon & David F. Hendry, 2016. "Detecting Volcanic Eruptions In Temperature Reconstructions By Designed Break-Indicator Saturation," Journal of Economic Surveys, Wiley Blackwell, vol. 30(3), pages 403-429, July.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Hendry, David F. & Mizon, Grayham E., 2014. "Unpredictability in economic analysis, econometric modeling and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 186-195.
    4. Solomon M. Hsiang & Daiju Narita, 2012. "Adaptation To Cyclone Risk: Evidence From The Global Cross-Section," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 3(02), pages 1-28.
    5. Eric Strobl, 2011. "The Economic Growth Impact of Hurricanes: Evidence from U.S. Coastal Counties," The Review of Economics and Statistics, MIT Press, vol. 93(2), pages 575-589, May.
    6. Søren Johansen & Bent Nielsen, 2016. "Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 321-348, June.
    7. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
    8. Jennifer L. Castle & David F. Hendry & Andrew B. Martinez, 2017. "Evaluating Forecasts, Narratives and Policy Using a Test of Invariance," Econometrics, MDPI, Open Access Journal, vol. 5(3), pages 1-27, September.
    9. Tatyana Deryugina & Laura Kawano & Steven Levitt, 2018. "The Economic Impact of Hurricane Katrina on Its Victims: Evidence from Individual Tax Returns," American Economic Journal: Applied Economics, American Economic Association, vol. 10(2), pages 202-233, April.
    10. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," Review of Economic Studies, Oxford University Press, vol. 81(2), pages 608-650.
    11. Tatyana Deryugina, 2017. "The Fiscal Cost of Hurricanes: Disaster Aid versus Social Insurance," American Economic Journal: Economic Policy, American Economic Association, vol. 9(3), pages 168-198, August.
    12. Morten O. Ravn & Harald Uhlig, 2002. "On adjusting the Hodrick-Prescott filter for the frequency of observations," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 371-375.
    13. Carolyn A. Dehring & Martin Halek, 2013. "Coastal Building Codes and Hurricane Damage," Land Economics, University of Wisconsin Press, vol. 89(4), pages 597-613.
    14. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    15. Davlasheridze, Meri & Fisher-Vanden, Karen & Allen Klaiber, H., 2017. "The effects of adaptation measures on hurricane induced property losses: Which FEMA investments have the highest returns?," Journal of Environmental Economics and Management, Elsevier, vol. 81(C), pages 93-114.
    16. William D. Nordhaus, 2010. "The Economics Of Hurricanes And Implications Of Global Warming," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 1(01), pages 1-20.
    17. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    18. Carlos Santos & David Hendry & Soren Johansen, 2008. "Automatic selection of indicators in a fully saturated regression," Computational Statistics, Springer, vol. 23(2), pages 317-335, April.
    19. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164.
    20. Julia Campos & David F. Hendry & Hans‐Martin Krolzig, 2003. "Consistent Model Selection by an Automatic Gets Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 803-819, December.
    21. Jurgen A. Doornik & Henrik Hansen, 2008. "An Omnibus Test for Univariate and Multivariate Normality," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 927-939, December.
    22. Engle, Robert F. & Hendry, David F., 1993. "Testing superexogeneity and invariance in regression models," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 119-139, March.
    23. Neil R. Ericsson, 2001. "Forecast uncertainty in economic modeling," International Finance Discussion Papers 697, Board of Governors of the Federal Reserve System (U.S.).
    24. Nicole Cornell Sadowski & Daniel Sutter, 2005. "Hurricane Fatalities and Hurricane Damages: Are Safer Hurricanes More Damaging?," Southern Economic Journal, Southern Economic Association, vol. 72(2), pages 422-432, October.
    25. Hendry, David F. & Johansen, Søren, 2015. "Model Discovery And Trygve Haavelmo’S Legacy," Econometric Theory, Cambridge University Press, vol. 31(1), pages 93-114, February.
    26. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Felix Pretis, 2015. "Detecting Location Shifts during Model Selection by Step-Indicator Saturation," Econometrics, MDPI, Open Access Journal, vol. 3(2), pages 1-25, April.
    27. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    28. Michael P. Clements, 2014. "Forecast Uncertainty- Ex Ante and Ex Post : U.S. Inflation and Output Growth," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 206-216, April.
    29. Justin Gallagher & Daniel Hartley, 2017. "Household Finance after a Natural Disaster: The Case of Hurricane Katrina," American Economic Journal: Economic Policy, American Economic Association, vol. 9(3), pages 199-228, August.
    30. Donald T. Resio & Nancy Powell & Mary Cialone & Himangshu S. Das & Joannes J. Westerink, 2017. "Quantifying impacts of forecast uncertainties on predicted storm surges," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(3), pages 1423-1449, September.
    31. Eva Regnier, 2008. "Public Evacuation Decisions and Hurricane Track Uncertainty," Management Science, INFORMS, vol. 54(1), pages 16-28, January.
    32. Timothy K. M. Beatty & Jay P. Shimshack & Richard J. Volpe, 2019. "Disaster Preparedness and Disaster Response: Evidence from Sales of Emergency Supplies Before and After Hurricanes," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 6(4), pages 633-668.
    33. Jonas Peters & Peter Bühlmann & Nicolai Meinshausen, 2016. "Causal inference by using invariant prediction: identification and confidence intervals," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(5), pages 947-1012, November.
    34. Jurgen A. Doornik, 2008. "Encompassing and Automatic Model Selection," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 915-925, December.
    35. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    36. Morana, Claudio & Sbrana, Giacomo, 2019. "Climate change implications for the catastrophe bonds market: An empirical analysis," Economic Modelling, Elsevier, vol. 81(C), pages 274-294.
    37. Adam Smith & Richard Katz, 2013. "US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 67(2), pages 387-410, June.
    38. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    39. Søren Johansen & Bent Nielsen, 2016. "Rejoinder: Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 374-381, June.
    40. James Mitchell & Kenneth F. Wallis, 2011. "Evaluating density forecasts: forecast combinations, model mixtures, calibration and sharpness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 1023-1040, September.
    41. Laura A. Bakkensen & Robert O. Mendelsohn, 2016. "Risk and Adaptation: Evidence from Global Hurricane Damages and Fatalities," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 3(3), pages 555-587.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David F. Hendry & Grayham E. Mizon, 2016. "Improving the teaching of econometrics," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170096-117, December.
    2. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Robust Discovery of Regression Models," Economics Papers 2020-W04, Economics Group, Nuffield College, University of Oxford.
    3. Ericsson, Neil R., 2017. "How biased are U.S. government forecasts of the federal debt?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 543-559.
    4. Felix Pretis & Lea Schneider & Jason E. Smerdon & David F. Hendry, 2016. "Detecting Volcanic Eruptions In Temperature Reconstructions By Designed Break-Indicator Saturation," Journal of Economic Surveys, Wiley Blackwell, vol. 30(3), pages 403-429, July.
    5. David F. Hendry & Felix Pretis, 2013. "Anthropogenic influences on atmospheric CO2," Chapters, in: Roger Fouquet (ed.), Handbook on Energy and Climate Change, chapter 12, pages 287-326, Edward Elgar Publishing.
    6. Ericsson, Neil R., 2017. "Economic forecasting in theory and practice: An interview with David F. Hendry," International Journal of Forecasting, Elsevier, vol. 33(2), pages 523-542.
    7. Jennifer Castle & David Hendry, 2016. "Policy Analysis, Forediction, and Forecast Failure," Economics Series Working Papers 809, University of Oxford, Department of Economics.
    8. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    9. Jurgen A. Doornik & David F. Hendry & Steve Cook, 2015. "Statistical model selection with “Big Data”," Cogent Economics & Finance, Taylor & Francis Journals, vol. 3(1), pages 1045216-104, December.
    10. Ericsson Neil R., 2016. "Testing for and estimating structural breaks and other nonlinearities in a dynamic monetary sector," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 377-398, September.
    11. Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.
    12. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Ragnar Nymoen, 2014. "Misspecification Testing: Non-Invariance of Expectations Models of Inflation," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 553-574, August.
    13. Ericsson, Neil R., 2017. "Interpreting estimates of forecast bias," International Journal of Forecasting, Elsevier, vol. 33(2), pages 563-568.
    14. Jennifer L. Castle & David F. Hendry & Andrew B. Martinez, 2017. "Evaluating Forecasts, Narratives and Policy Using a Test of Invariance," Econometrics, MDPI, Open Access Journal, vol. 5(3), pages 1-27, September.
    15. David F. Hendry, 2020. "A Short History of Macro-econometric Modelling," Economics Papers 2020-W01, Economics Group, Nuffield College, University of Oxford.
    16. Hendry, David F. & Mizon, Grayham E., 2014. "Unpredictability in economic analysis, econometric modeling and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 186-195.
    17. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    18. Stillwagon, Josh R., 2016. "Non-linear exchange rate relationships: An automated model selection approach with indicator saturation," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 84-109.
    19. Castle, Jennifer L. & Hendry, David F., 2014. "Model selection in under-specified equations facing breaks," Journal of Econometrics, Elsevier, vol. 178(P2), pages 286-293.
    20. David Hendry & Grayham E. Mizon, 2012. "Forecasting from Structural Econometric Models," Economics Series Working Papers 597, University of Oxford, Department of Economics.

    More about this item

    Keywords

    adaptation; model selection; natural disasters; uncertainty;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jecnmx:v:8:y:2020:i:2:p:18-:d:357835. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (XML Conversion Team). General contact details of provider: https://www.mdpi.com/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.