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Confronting collinearity in environmental regression models: evidence from world data

机译：在环境回归模型中面对联合性：来自世界数据的证据

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摘要

Despite the evidence, the correlation between environmental impact factors has mostly been neglected in econometric environmental models or treated with traditional methodologies such as ridge regression, which are recommended when the goal is prediction and the estimated parameters are not interpreted as causal effects. This paper addresses the existing collinearity with alternative methodologies, not only to mitigate the problem mechanically, but also to isolate the effects of the environmental impact factors with the main objective of designing better policies for countries. The methodologies are applied to analyze the CO2 emissions of 114 countries covering the thirteen most recent years with available data, and the results from the empirical and methodological perspectives are compared. The treatment of collinearity with the residualization or raise regression procedures allows the researcher to obtain a global vision of the relationship between the different factors affecting CO2 emissions, thus reaching alternative conclusions to those from traditional methodologies.
机译：尽管证据表明，环境影响因素之间的相关性主要在经济学环境模型中忽略了忽视，或者用传统方法如山脊回归处理，当目标是预测时建议使用，并且估计的参数不会被解释为因果效应。本文以替代方法解决了现有的共同性，不仅要在机械上减轻问题，而且还可以将环境影响因素的影响与为各国设计更好的政策而抵消。该方法适用于分析114个国家的二氧化碳排放，涵盖十三年的最近几年，比较了实证和方法视角的结果。通过残留或提高回归程序的共同性治疗允许研究人员获得影响二氧化碳排放的不同因素之间的关系的全球视野，从而向传统方法的替代结论达到了替代方案。

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