A Density-Ratio Model of Crop Yield Distributions

This paper proposes a density ratio estimator of crop yield distributions, wherein the number of observations for individual distributions is often quite small. The density ratio approach models individual densities as distortions from a common baseline density. We introduce a probability integral transformation to the density ratio method that simplifies the modeling of distortion functions. We further present an implementation approach based on the Poisson regression, which facilitates model estimation and diagnostics. Monte Carlo simulations demonstrate good finite sample performance of the proposed method. We apply this method to estimate the corn yield distributions of ninety-nine Iowa counties, and to calculate crop insurance premiums. Lastly, we illustrate that we can employ the proposed method to effectively identify profitable insurance policies.