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Gaussian process modeling of nonstationary crop yield distributions with applications to crop insurance.
- Source :
-
Agricultural Finance Review . 2021, Vol. 81 Issue 5, p767-783. 17p. - Publication Year :
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00021466
- Volume :
- 81
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Agricultural Finance Review
- Publication Type :
- Academic Journal
- Accession number :
- 152676043
- Full Text :
- https://doi.org/10.1108/AFR-09-2020-0144