摘要：The objective of this article is to propose the use of moment functions and maximum entropy techniques as a flexible way to estimate conditional crop yield distributions. We present a moment based model that extends previous approaches in several dimensions, and can be easily estimated using standard econometric estimators. Upon identification of the yield moments under a variety of climate and irrigation regimes, we utilize maximum entropy techniques to analyze the distributional impacts from switching regimes. We consider the case of Arkansas, Mississippi, and Texas upland cotton to demonstrate how climate and irrigation affect the shape of the yield distribution, and compare our findings to other moment based approaches. We empirically illustrate several advantages of our moment based maximum entropy approach, including flexibility of the distributional tails across alternative irrigation and climate regimes.
