Nonparametric Estimation of Crop Insurance Rates Revisited
用新方法估计条件产量密度并推导保险费率，发现经验贝叶斯非参数核密度估计器比传统核密度估计器节省26年产量数据，有助于解决数据不足问题。
Abstract With the crop insurance program becoming the cornerstone of U.S. agricultural policy, recovering accurate rates is of paramount interest. Lack of yield data presents, by far, the most fundamental obstacle to recovery of accurate rates. This article employs new methodology to estimate conditional yield densities and derive the insurance rates. In our application, we find the nonparametric kernel density estimator requires an additional twenty‐six years of yield data to estimate the shape of the conditional yield densities as accurately as the recently developed empirical Bayes nonparametric kernel density estimator. Such methodological improvements can significantly aid in ameliorating the data problem.