Premium
Nonparametric Estimation of Crop Insurance Rates Revisited
Author(s) -
Ker Alan P.,
Goodwin Barry K.
Publication year - 2000
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.1111/0002-9092.00039
Subject(s) - nonparametric statistics , kernel density estimation , crop insurance , estimator , econometrics , yield (engineering) , estimation , statistics , bayes' theorem , density estimation , economics , kernel (algebra) , mathematics , agriculture , bayesian probability , geography , materials science , management , archaeology , combinatorics , metallurgy
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.