Estimation of Yield Densities: A Bayesian Nonparametric Perspective

The pricing of crop insurance products hinges crucially on the accurate estimation of the underlying yield densities. Multiple estimation methods have already been examined in the literature, but the need for other potential candidates remains essential. Here we propose and examine a Bayesian nonparametric model which is based on Dirichlet processes for yield estimation. We deploy our proposed model for the empirical estimation of county level yield data for Cotton from Texas. Next, we examine the implications of our modeling framework on the pricing of the Group Risk Plan (GRP) insurance compared to a nonparametric Kernel-type model.


Issue Date:
2015
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/204325
JEL Codes:
C11; Q18
Series Statement:
Poster
6401




 Record created 2017-04-01, last modified 2017-08-28

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