Assessing the Impact of COVID-19 on Trade: a Machine Learning Counterfactual Analysis

Marco Due\~nas, Victor Ortiz Gimenez (), Massimo Riccaboni and Francesco Serti
Authors registered in the RePEc Author Service: Marco Dueñas

Papers from arXiv.org

Abstract: By interpreting exporters' dynamics as a complex learning process, this paper constitutes the first attempt to investigate the effectiveness of different Machine Learning (ML) techniques in predicting firms' trade status. 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. By comparing the resulting predictions, we estimate the individual treatment effect of the COVID-19 shock on firms' outcomes. Finally, we use recursive partitioning methods to identify subgroups with differential treatment effects. We find that, besides the temporal dimension, the main factors predicting treatment heterogeneity are interactions between firm size and industry.

Date: 2021-04
New Economics Papers: this item is included in nep-big, nep-cmp, nep-int and nep-sbm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Assessing the Impact of COVID-19 on Trade: a Machine Learning Counterfactual Analysis (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2104.04570

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