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

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 tend to be applied at the county level only. 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 e±ciency gains. We find that when we allow insurance companies to strategically reinsure with the government based on this novel approach, it becomes quite clear that they accrue significant rents.

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Jeff Racine and Alan P. Ker

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