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| Racine, Jeffrey S.; Ker, Alan P.. |
| 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... |
| Tipo: Journal Article |
Palavras-chave: Discrete data; Insurance rating; Kernel estimation; Yield distributions; Risk and Uncertainty. |
| Ano: 2006 |
URL: http://purl.umn.edu/10146 |
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