A Density-Ratio Model of Crop Yield Distributions

Abstract

This paper proposes a flexible estimator of crop yield distributions, wherein data typically consist of a short panel of many cross-sectional units. The density ratio estimator is characterized by a common baseline density and individual densities modeled as distortions from the baseline. Key features of the proposed estimator include efficiency gains from information pooling, flexibility in both the baseline and individual densities, and a parsimonious parametrization via the probability integration transformation. We further present an estimation approach based on Poisson regression, which is computationally simple and facilitates model diagnostics and inferences. We demonstrate outstanding finite sample performance of the proposed method using Monte Carlo simulations and apply it to estimate the corn yield distributions of 99 Iowa counties. While separate estimation of individual densities suffers from large sampling variations, the proposed density-ratio model produces reliable estimates that accurately capture the spatial clustering among the data.