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arXiv 2104.04570econ.GNcs.LGq-fin.EC
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评估经济全面冲击的异质性影响：一种应用于哥伦比亚企业的机器学习方法

Assessing the Heterogeneous Impact of Economy-Wide Shocks: A Machine Learning Approach Applied to Colombian Firms

CLIMAFIN – Climate Finance Alpha（CLIMAFIN气候金融阿尔法机构）；IMT School for Advanced Studies Lucca（卢卡高等研究院）；Central Bank of Colombia（哥伦比亚中央银行）

Marco Dueñas, Federico Nutarelli, Víctor Ortiz, Massimo Riccaboni, Francesco Serti

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42 pages, 13 figures
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AI总结：

本文提出一种机器学习方法，评估经济全面冲击的异质性影响，并应用于COVID-19对哥伦比亚企业出口生存概率的影响，发现平均降低约20个百分点，并分析处理效应异质性。

摘要：

我们的论文提出了一种研究经济全面冲击异质性效应的方法论，并将其应用于COVID-19危机对出口影响的情形。该方法适用于冲击的普遍性阻碍了识别未受冲击影响的对照组，以及无法事先定义每个单位受冲击暴露强度的场景。具体而言，我们的研究考察了各种机器学习（ML）技术在预测企业贸易方面的有效性，并基于因果机器学习的最新进展，利用这些预测来重构不同COVID-19情景下企业贸易的反事实分布，并研究处理效应的异质性。我们特别关注哥伦比亚企业在两种不同情景下在出口市场中生存的概率：一种是有COVID-19的情景，另一种是无COVID-19的反事实情景。平均而言，我们发现COVID-19冲击在2020年4月使企业继续在出口市场生存的概率下降了约20个百分点。我们通过采用分类分析来研究处理效应的异质性，该分析比较了估计的个体处理效应分布尾端企业的特征。

英文摘要：

Our paper presents a methodology to study the heterogeneous effects of economy-wide shocks and applies it to the case of the impact of the COVID-19 crisis on exports. This methodology is applicable in scenarios where the pervasive nature of the shock hinders the identification of a control group unaffected by the shock, as well as the ex-ante definition of the intensity of the shock's exposure of each unit. In particular, our study investigates the effectiveness of various Machine Learning (ML) techniques in predicting firms' trade and, by building on recent developments in causal ML, uses these predictions to reconstruct the counterfactual distribution of firms' trade under different COVID-19 scenarios and to study treatment effect heterogeneity. Specifically, we focus on the probability of Colombian firms surviving in the export market under two different scenarios: a COVID-19 setting and a non-COVID-19 counterfactual situation. On average, we find that the COVID-19 shock decreased a firm's probability of surviving in the export market by about 20 percentage points in April 2020. We study the treatment effect heterogeneity by employing a classification analysis that compares the characteristics of the firms on the tails of the estimated distribution of the individual treatment effects.

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。

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