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Essays on Estimating Conditional Crop Yield Densities and Rating Crop Insurance Contracts

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Title: Essays on Estimating Conditional Crop Yield Densities and Rating Crop Insurance Contracts
Author: Liu, Yong
Department: Department of Food, Agricultural and Resource Economics
Program: Food, Agriculture and Resource Economics
Advisor: Ker, Alan
Abstract: This dissertation consists of four essays that focus on the U.S. crop insurance program, particularly econometric methodologies for estimating conditional crop yield densities and corresponding crop insurance premium rates in the U.S. This is of economic significance because the U.S. crop insurance program has been the pillar of U.S. domestic agricultural policy for the past 25 years. The first essay contributes to the literature by investigating the efficacy of using nonparametric Bayesian Model Averaging (BMA) to incorporate extraneous information into the estimated premium rates. Nonparametric BMA is particularly suited to this application because it does not make any assumptions about parametric form or to what extent yields are similar. The nonparametric BMA consistently decreases error and enables statistically and economically significant rents to be captured. The second essay contributes to the literature by refining the above methodology to account for various levels of spatial closeness. Specifically, the proposed extension continuously refines the estimate based on hierarchical spatial structure of geographic features in U.S. crop production: multi-state, state and crop reporting district(CRD). Results indicate that significant improvement in stability and accuracy of premium rate are delivered by the proposed method. The third essay contributes to the literature by exploring whether governments/insurers should or should not historically trim yields in estimating their premium rates. Distributional tests and an out-of-sample retain-cede rating game are used to answer this question. Despite small sample sizes and the need to estimate tail probabilities, the historical data appears to be sufficiently different such that trimming is justified. The final essay contributes to the literature by extending the above methodologies to simultaneously incorporate extraneous yield information from both space and time. This essay proposes three successively flexible data-driven methodologies to nonparametrically smooth across both space and time simultaneously. By applying these methodologies in estimating U.S. corn and soybean county-level crop insurance premium rates, we found significant borrowing of information across both time and space. These three methodologies also improve both the stability and accuracy of crop insurance premium rates.
Date: 2019-09
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
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Attribution-NonCommercial-NoDerivatives 4.0 International Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International