相对重要性分析的混合计量经济学-机器学习方法：优先考虑粮食政策
A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy
摘要：
当计量经济学方法的解释方面感兴趣时，研究人员经常需要测量变量的相对重要性。为此，作者简要回顾了传统计量经济学在构建变量重要性的可靠度量方面的局限性。作者强调了解释性和预测性分析在经济学中的相对地位，以及计量经济学和计算机科学之间富有成效的合作的出现。从两者中吸取教训，作者提出了一种基于传统计量经济学和高级机器学习（ML）算法的混合方法，这些算法在预测分析中使用。本文的目的是双重的，提出了一种混合方法来评估相对重要性，并以印度的食品通胀为例证明其在解决政策优先事项问题方面的适用性，然后是一个更广泛的目标，即向经济学和社会科学研究人员介绍ML和传统计量经济学合并的可能性。
英文摘要：
A measure of relative importance of variables is often desired by researchers when the explanatory aspects of econometric methods are of interest. To this end, the author briefly reviews the limitations of conventional econometrics in constructing a reliable measure of variable importance. The author highlights the relative stature of explanatory and predictive analysis in economics and the emergence of fruitful collaborations between econometrics and computer science. Learning lessons from both, the author proposes a hybrid approach based on conventional econometrics and advanced machine learning (ML) algorithms, which are otherwise, used in predictive analytics. The purpose of this article is two-fold, to propose a hybrid approach to assess relative importance and demonstrate its applicability in addressing policy priority issues with an example of food inflation in India, followed by a broader aim to introduce the possibility of conflation of ML and conventional econometrics to an audience of researchers in economics and social sciences, in general.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
