转基因玉米的风险效应:来自作物保险结果和高维方法的证据

IF 4.5 3区 经济学 Q1 AGRICULTURAL ECONOMICS & POLICY
Serkan Aglasan, Barry K. Goodwin, Roderick M. Rejesus
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引用次数: 0

摘要

本研究评价了具有抗根虫性状的转基因玉米(GM- rw)是否具有较低产量风险。作物保险精算绩效指标损失成本比(LCR)被用来表示产量风险。本研究采用高维方法,以保持经验规范的简洁性,并便于估计。具体来说,我们采用Cluster-Lasso (cluster-最小绝对收缩和选择操作符)程序。在高维面板数据设置中,即使存在异方差、非高斯和聚类误差结构,该方法也会对感兴趣的主要变量(即GM-RW变量)产生一致有效的推断。在使用Cluster-Lasso控制了大量潜在的天气混杂因素后,我们发现了一致的证据,即具有抗根虫性状的转基因玉米杂交品种具有较低的产量风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Risk effects of GM corn: Evidence from crop insurance outcomes and high-dimensional methods

This study evaluates whether genetically modified (GM) corn hybrids with rootworm resistant traits (GM-RW) have lower yield risk. A crop insurance actuarial performance measure, the loss cost ratio (LCR), is used to represent yield risk. High-dimensional methods are utilized in this study to maintain parsimony in the empirical specification, and facilitate estimation. Specifically, we employ the Cluster-Lasso (cluster-least absolute shrinkage and selection operator) procedure. This method produces uniformly valid inference on the main variable of interest (i.e., the GM-RW variable) in a high-dimensional panel data setting even in the presence of heteroskedastic, non-Gaussian, and clustered error structures. After controlling for a large set of potential weather confounders using Cluster-Lasso, we find consistent evidence that GM corn hybrids with rootworm resistant traits have lower yield risk.

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来源期刊
Agricultural Economics
Agricultural Economics 管理科学-农业经济与政策
CiteScore
7.30
自引率
4.90%
发文量
62
审稿时长
3 months
期刊介绍: Agricultural Economics aims to disseminate the most important research results and policy analyses in our discipline, from all regions of the world. Topical coverage ranges from consumption and nutrition to land use and the environment, at every scale of analysis from households to markets and the macro-economy. Applicable methodologies include econometric estimation and statistical hypothesis testing, optimization and simulation models, descriptive reviews and policy analyses. We particularly encourage submission of empirical work that can be replicated and tested by others.
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