ExtremeBounds: R中的极限界分析

M. Hlaváč
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引用次数: 28

摘要

本文介绍了R包ExtremeBounds来执行极端边界分析(extreme bounds analysis, EBA),这是一种灵敏度测试,用于检查回归模型的因变量与各种可能的决定因素的相关性。ExtremeBounds支持Leamer的EBA,它关注回归系数的上界和下界,以及Sala-i-Martin的EBA,它考虑回归系数的整个分布。与现有的替代方法相比,它可以估计各种用户定义大小的模型,使用除普通最小二乘之外的回归模型,在模型规范中合并非线性,并应用自定义权重和标准误差。为了减轻对所检查变量的多重共线性和概念重叠的担忧,ExtremeBounds允许用户指定互斥变量集,并且可以将分析限制为来自回归模型的系数,这些模型在预先指定的限制内产生方差膨胀因子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ExtremeBounds: Extreme Bounds Analysis in R
This article introduces the R package ExtremeBounds to perform extreme bounds analysis (EBA), a sensitivity test that examines how robustly the dependent variable of a regression model is related to a variety of possible determinants. ExtremeBounds supports Leamer's EBA that focuses on the upper and lower extreme bounds of regression coefficients, as well as Sala-i-Martin's EBA which considers their entire distribution. In contrast to existing alternatives, it can estimate models of a variety of user-defined sizes, use regression models other than ordinary least squares, incorporate non-linearities in the model specification, and apply custom weights and standard errors. To alleviate concerns about the multicollinearity and conceptual overlap of examined variables, ExtremeBounds allows users to specify sets of mutually exclusive variables, and can restrict the analysis to coefficients from regression models that yield a variance inflation factor within a prespecified limit.
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