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

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.