Rating Crop Insurance Policies with Efficient Nonparametric Estimators that Admit Mixed Data Types
Jeffrey Racine () and
Alan Ker ()
Journal of Agricultural and Resource Economics, 2006, vol. 31, issue 1, 13
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.
Keywords: Risk; and; Uncertainty (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:jlaare:10146
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