More than Mean Effects: Modeling the Effect of Climate on the Higher Order Moments of Crop Yields
提出一种基于矩函数和最大熵的方法来估计作物产量的条件分布，用于分析气候和灌溉如何影响产量分布的形状，并以美国三州棉花为例展示其优势。
This article proposes the use of moment functions and maximum entropy techniques as a flexible approach for estimating conditional crop yield distributions. We present a moment‐based model that extends previous approaches, and is easily estimated using standard econometric estimators. Predicted moments under alternative regimes are used as constraints in a maximum entropy framework to analyze the distributional impacts of switching regimes. An empirical application for Arkansas, Mississippi, and Texas upland cotton demonstrates how climate and irrigation affect the shape of the yield distribution, and allows us to illustrate several advantages of our moment‐based maximum entropy approach.