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  1. Predicting fiscal crises
    a machine learning approach
    Author: Hellwig, Klaus-Peter
    Published: May 2021
    Publisher:  International Monetary Fund, [Washington, D.C.]

    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.... more

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    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

     
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    Source: Staatsbibliothek zu Berlin
    Language: English
    Media type: Ebook
    Format: Online
    ISBN: 9781513573588
    Other identifier:
    Series: IMF working paper ; WP/21, 150
    Subjects: Early warning systems; sovereign default; random forest; Foreign Exchange; Informal Economy; Underground Econom
    Scope: 1 Online-Ressource (circa 66 Seiten), Illustrationen
  2. Predicting fiscal crises
    a machine learning approach
    Author: Hellwig, Klaus-Peter
    Published: May 2021
    Publisher:  International Monetary Fund, [Washington, D.C.]

    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.... more

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    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

     
    Export to reference management software   RIS file
      BibTeX file
    Source: Staatsbibliothek zu Berlin
    Language: English
    Media type: Ebook
    Format: Online
    ISBN: 9781513573588
    Other identifier:
    Series: IMF working paper ; WP/21, 150
    Subjects: Early warning systems; sovereign default; random forest; Foreign Exchange; Informal Economy; Underground Econom; Macroeconomics; Economics: General; International Economics; Economic & financial crises & disasters; Economics of specific sectors; Currency crises; Informal sector; Economics
    Scope: 1 Online-Ressource (circa 66 Seiten), Illustrationen