Linear pooling of potentially related density forecasts in crop insurance

Abstract

Accurate pricing of crop insurance policies relies on forecasts of probability densities of crop yields. Yield densities are dynamic, time series data on yields are often limited, and yield data are spatially correlated. We examine linear pooling of potentially related, but almost surely misspecified, crop yield density forecasts. The pooled forecasts combine densities from other spatial units based on out-of-sample forecast performance. The pooled densities result in more accurate premium rates which can reduce incentives for adverse selection. The approach is applicable to any insurance setting where the statistical model for the loss variable is likely to be misspecified and the underlying data-generating processes are potentially related.