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Gaussian process modeling of nonstationary crop yield distributions with applications to crop insurance

Wenbin Wu (Hulu LLC, Santa Monica, California, USA)
Ximing Wu (Agricultural Economics, Texas A&M University, College Station, Texas, USA)
Yu Yvette Zhang (Agricultural Economics, Texas A&M University, College Station, Texas, USA)
David Leatham (Agricultural Economics, Texas A&M University, College Station, Texas, USA)

Agricultural Finance Review

ISSN: 0002-1466

Article publication date: 23 February 2021

Issue publication date: 29 September 2021

245

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

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