Gaussian process modeling of nonstationary crop yield distributions with applications to crop insurance
ISSN: 0002-1466
Article publication date: 23 February 2021
Issue publication date: 29 September 2021
Abstract
Purpose
The purpose of this paper is to bring out the development of a flexible model for nonstationary crop yield distributions and its applications to decision-making in crop insurance.
Design/methodology/approach
The authors design a nonparametric Bayesian approach based on Gaussian process regressions to model crop yields over time. Further flexibility is obtained via Bayesian model averaging that results in mixed Gaussian processes.
Findings
Simulation results on crop insurance premium rates show that the proposed method compares favorably with conventional estimators, especially when the underlying distributions are nonstationary.
Originality/value
Unlike conventional two-stage estimation, the proposed method models nonstationary crop yields in a single stage. The authors further adopt a decision theoretic framework in its empirical application and demonstrate that insurance companies can use the proposed method to effectively identify profitable policies under symmetric or asymmetric loss functions.
Keywords
Acknowledgements
Portions of this research were conducted with the advanced computing resources provided by Texas A&M High Performance Research Computing.
Citation
Wu, W., Wu, X., Zhang, Y.Y. and Leatham, D. (2021), "Gaussian process modeling of nonstationary crop yield distributions with applications to crop insurance", Agricultural Finance Review, Vol. 81 No. 5, pp. 767-783. https://doi.org/10.1108/AFR-09-2020-0144
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited