IMF Working Papers

Predicting Fiscal Crises: A Machine Learning Approach

By Klaus-Peter Hellwig

May 27, 2021

Download PDF

Preview Citation

Format: Chicago

Klaus-Peter Hellwig. Predicting Fiscal Crises: A Machine Learning Approach, (USA: International Monetary Fund, 2021) accessed October 5, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb. Machine learning techniques (elastic net, random forest, gradient boosted trees) deliver significant improvements in accuracy. Performance of machine learning techniques improves further, particularly for developing countries, when I expand the set of potential predictors and make use of algorithmic selection techniques instead of relying on a small set of variables deemed important by the literature. There is considerable agreement across learning algorithms in the set of selected predictors: Results confirm the importance of external sector stock and flow variables found in the literature but also point to demographics and the quality of governance as important predictors of fiscal crises. Fiscal variables appear to have less predictive value, and public debt matters only to the extent that it is owed to external creditors.

Publication Details

  • Pages:

    66

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Working Paper No. 2021/150

  • Stock No:

    WPIEA2021150

  • ISBN:

    9781513573588

  • ISSN:

    1018-5941