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Abstract
The objective of this study is to evaluate and model the yield risk associated
with major agricultural commodities in the U.S. We are particularly concerned
with the nonstationary nature of the yield distribution, which primarily arises
because of technological progress and changing environmental conditions. Precise
risk assessment depends on the accuracy of modeling this distribution. This problem
becomes more challenging as the yield distribution changes over time, a condition
that holds for nearly all major crops. A common approach to this problem
is based on a two-stage method in which the yield is first detrended and then
the estimated residuals are treated as observed data and modeled using various
parametric or nonparametric methods. We propose an alternative parametric
model that allows the moments of the yield distributions to change with time.
Several model selection techniques suggest that the proposed time-varying model
outperforms more conventional models in terms of in-sample goodness-of-fit, out-of-
sample predictive power and the prediction accuracy of insurance premium rates.