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Rating Crop Insurance Policies with Efficient Nonparametric Estimators that Admit Mixed Data Types

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  • Racine, Jeffrey S.
  • Ker, Alan P.

Abstract

The identification of improved methods for characterizing crop yield densities has experienced a recent surge in activity due in part to the central role played by crop insurance in the Agricultural Risk Protection Act of 2000 (estimates of yield densities are required for the determination of insurance premium rates). Nonparametric kernel methods have been successfully used to model yield densities; however, traditional kernel methods do not handle the presence of categorical data in a satisfactory manner and have therefore tended to be applied on a county-by-county basis. By utilizing recently developed kernel methods that admit mixed data types, we are able to model the yield density jointly across counties, leading to substantial finite sample efficiency gains. Findings show that when we allow insurance companies to strategically reinsure with the government based on this novel approach they accrue significant rents.

Suggested Citation

  • Racine, Jeffrey S. & Ker, Alan P., 2006. "Rating Crop Insurance Policies with Efficient Nonparametric Estimators that Admit Mixed Data Types," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 31(1), pages 1-13, April.
    • Handle: RePEc:ags:jlaare:10146
    DOI: 10.22004/ag.econ.10146
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    Citations

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    Cited by:

    1. Zongyuan Shang & Alan Ker, 2021. "Two generalized nonparametric methods for estimating like densities," Computational Statistics, Springer, vol. 36(1), pages 113-126, March.
    2. Wilson, William W. & Gustafson, Cole R. & Dahl, Bruce L., 2006. "Production Risk And Crop Insurance In Malting Barley: A Stochastic Dominance Analysis," Agribusiness & Applied Economics Report 23561, North Dakota State University, Department of Agribusiness and Applied Economics.
    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. Liu, Y. & Ker, A., 2018. "Is There Too Much History in Historical Yield Data," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277293, International Association of Agricultural Economists.
    5. Ker, Alan. P & Tolhurst, Tor & Liu, Yong, 2015. "Rating Area-yield Crop Insurance Contracts Using Bayesian Model Averaging and Mixture Models," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205211, Agricultural and Applied Economics Association.
    6. 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.
    7. 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.
    8. 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.

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    Keywords

    Risk and Uncertainty;

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