Using Bayesian Kriging for Spatial Smoothing in Crop Insurance Rating

Abstract Rating insurance policies depends on the probability of events in the tail of a distribution. A method to measure such tail-related risk based on Extreme Value Theory could potentially improve insurance rating. It is also widely agreed that there is a spatial structure to crop yield distrib...

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Bibliographic Details
Published in:American journal of agricultural economics 2019-01, Vol.101 (1), p.330-351
Main Authors: Park, Eunchun, Brorsen, B Wade, Harri, Ardian
Format: Article
Language:English
Subjects:
Agricultural economics
Agricultural production
Area yield insurance rating
Bayesian analysis
Bayesian Kriging
Bayesian spatial smoothing
Crop insurance
Crop yield
Crops
Distribution
Extreme value theory
Extreme values
Gaussian spatial process
Government agencies
Insurance
Insurance policies
Mathematical models
Parameter estimation
Policies
Probability
Ratings & rankings
spatial econometrics
Spatial smoothing
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Summary:Abstract Rating insurance policies depends on the probability of events in the tail of a distribution. A method to measure such tail-related risk based on Extreme Value Theory could potentially improve insurance rating. It is also widely agreed that there is a spatial structure to crop yield distributions. Considering the spatial structure may provide more precisely rated policies. In this context, this research provides two contributions in rating area yield crop insurance. One is to provide a method that fits the tail of crop yield distributions using the Generalized Pareto Distribution (GPD), a member of the family of extreme value distributions that models only the tail of the distribution. The second is to estimate parameters of the distribution using a Bayesian Kriging approach that provides spatial smoothing of GPD parameters. The proposed model provides estimates of the spatial structure across regions such as the maximum distance of the spatial effect. Based on an out-of-sample performance game between a private insurance company and the federal agency the proposed model provides considerable improvement, particularly when rating deeper tail probability.
ISSN:0002-9092
1467-8276
DOI:10.1093/ajae/aay045