Rating Crop Insurance Policies with Efficient Nonparametric Estimators that Admit Mixed Data Types
AbstractThe 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.
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Bibliographic InfoArticle provided by Western Agricultural Economics Association in its journal Journal of Agricultural and Resource Economics.
Volume (Year): 31 (2006)
Issue (Month): 01 (April)
discrete data; insurance rating; kernel estimation; yield distributions; Risk and Uncertainty;
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- Gustafson, Cole R. & Wilson, William W. & Dahl, Bruce L., 2006.
"Production Risk And Crop Insurance In Malting Barley: A Stochastic Dominance Analysis,"
2006 Annual meeting, July 23-26, Long Beach, CA
21095, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
- 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.
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