摘要翻译：
当计量经济学方法的解释方面是感兴趣的时，研究人员往往希望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造变量重要性的可靠测度方面的局限性。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先进机器学习(ML)算法的混合方法，这些算法用于预测分析。本文的目的是双重的，提出一种混合方法来评估相对重要性，并以印度的食品通货膨胀为例说明其在解决政策优先问题中的适用性，其次是一个更广泛的目标，向一般的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。
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英文标题：
《A hybrid econometric-machine learning approach for relative importance
  analysis: Prioritizing food policy》
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作者：
Akash Malhotra
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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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.
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PDF链接：
https://arxiv.org/pdf/1806.04517