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Data and Code for: Emerging Markets at Risk

Version
1
Resource Type
Dataset
Creator
  • Asis, Gonzalo (University of North Carolina-Chapel Hill)
  • Chari, Anusha (University of North Carolina-Chapel Hill)
  • Haas, Adam (University of North Carolina-Chapel Hill)
Publication Date
2020-08-26
Funding Reference
  • NUS-RMI-CRI
Description
  • Abstract

    Policymakers would like to predict and mitigate the risks associated with the post-global financial crisis rise in corporate leverage in emerging markets. However, long-standing advanced-economy bankruptcy models fail to capture the idiosyncrasies that impact the solvency of emerging market firms. We study how a machine learning technique for variable selection, LASSO, can improve corporate distress risk models in emerging markets. Exploring the trade-off between model fit and predictive power, we find that larger models forecast distress with more accuracy during periods of economic stress (when global factors gain relevance), while more parsimonious specifications outperform during normal times
Availability
Download
This study is freely available to the general public via web download.
Relations
  • Is version of
    DOI: 10.3886/E120705
Publications
  • Asis, Gonzalo, Anusha Chari, and Adam Haas. “Emerging Markets at Risk.” AEA Papers and Proceedings 110 (May 2020): 493–98. https://doi.org/10.1257/pandp.20201007.
    • ID: 10.1257/pandp.20201007 (DOI)

Update Metadata: 2020-08-27 | Issue Number: 1 | Registration Date: 2020-08-27

Asis, Gonzalo; Chari, Anusha; Haas, Adam (2020): Data and Code for: Emerging Markets at Risk. Version: 1. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E120705V1