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Using Bayesian Kriging for Spatial Smoothing in Crop Insurance Rating

Author

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  • Eunchun Park
  • B Wade Brorsen
  • Ardian Harri

Abstract

Rating insurance policies depends on the probability of events in the tail of a distribution. A method to measure such tail-related risk based on Extreme Value Theory could potentially improve insurance rating. It is also widely agreed that there is a spatial structure to crop yield distributions. Considering the spatial structure may provide more precisely rated policies. In this context, this research provides two contributions in rating area yield crop insurance. One is to provide a method that fits the tail of crop yield distributions using the Generalized Pareto Distribution (GPD), a member of the family of extreme value distributions that models only the tail of the distribution. The second is to estimate parameters of the distribution using a Bayesian Kriging approach that provides spatial smoothing of GPD parameters. The proposed model provides estimates of the spatial structure across regions such as the maximum distance of the spatial effect. Based on an out-of-sample performance game between a private insurance company and the federal agency the proposed model provides considerable improvement, particularly when rating deeper tail probability.

Suggested Citation

  • Eunchun Park & B Wade Brorsen & Ardian Harri, 2019. "Using Bayesian Kriging for Spatial Smoothing in Crop Insurance Rating," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(1), pages 330-351.
    • Handle: RePEc:oup:ajagec:v:101:y:2019:i:1:p:330-351.
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    File URL: http://hdl.handle.net/10.1093/ajae/aay045
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    Citations

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    Cited by:

    1. Alex Boakye, 2023. "Estimating agriculture technologies’ impact on maize yield in rural South Africa," SN Business & Economics, Springer, vol. 3(8), pages 1-17, August.
    2. Niyibizi, Bart & Brorsen, Wade & Park, Eunchun, 2018. "Using Bayesian Kriging for Spatial Smoothing of Trends in the Means and Variances of Crop Yield Densities," 2018 Annual Meeting, August 5-7, Washington, D.C. 274403, Agricultural and Applied Economics Association.
    3. Yong Liu & Alan P. Ker, 2021. "Simultaneous borrowing of information across space and time for pricing insurance contracts: An application to rating crop insurance policies," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(1), pages 231-257, March.
    4. Yong Liu & A. Ford Ramsey, 2023. "Incorporating historical weather information in crop insurance rating," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(2), pages 546-575, March.
    5. Park, Eunchun & Harri, Ardian & Coble, Keith H., 2022. "Estimating Crop Yield Densities for Counties with Missing Data," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 47(3), September.
    6. Kuangyu Wen, 2023. "A semiparametric spatio‐temporal model of crop yield trend and its implication to insurance rating," Agricultural Economics, International Association of Agricultural Economists, vol. 54(5), pages 662-673, September.
    7. A. Ford Ramsey & Barry K. Goodwin, 2019. "Value-at-Risk and Models of Dependence in the U.S. Federal Crop Insurance Program," JRFM, MDPI, vol. 12(2), pages 1-21, April.
    8. A Ford Ramsey, 2020. "Probability Distributions of Crop Yields: A Bayesian Spatial Quantile Regression Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 220-239, January.
    9. Brian E. Mills & B. Wade Brorsen & Davood Poursina & D. Brian Arnall, 2023. "Optimal grid size for site‐specific nutrient application," Agricultural Economics, International Association of Agricultural Economists, vol. 54(6), pages 854-866, November.
    10. Cho, Whoi & Brorsen, B. Wade, 2021. "Design of the Rainfall Index Crop Insurance Program for Pasture, Rangeland, and Forage," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 46(1), January.
    11. Chunli Huang & Xu Zhao & Weihu Cheng & Qingqing Ji & Qiao Duan & Yufei Han, 2022. "Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors," Mathematics, MDPI, vol. 10(9), pages 1-25, April.
    12. repec:rre:publsh:v:51:y:2021:i:2 is not listed on IDEAS
    13. Kuangyu Wen & Ximing Wu & David J. Leatham, 2021. "Spatially Smoothed Kernel Densities with Application to Crop Yield Distributions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 349-366, September.
    14. Ceballos-Sierra, Federico & Dall'Erba, Sandy, 2021. "The effect of climate variability on Colombian coffee productivity: A dynamic panel model approach," Agricultural Systems, Elsevier, vol. 190(C).
    15. Xiaotao Li & Jinzheng Ren & Beibei Niu & Haiping Wu, 2020. "Grain Area Yield Index Insurance Ratemaking Based on Time–Space Risk Adjustment in China," Sustainability, MDPI, vol. 12(6), pages 1-15, March.

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