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Leading indicators of fiscal distress: evidence from extreme bounds analysis

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  • Martin Bruns
  • Tigran Poghosyan

Abstract

Early warning systems (EWSs) are widely used to assess a country’s vulnerability to fiscal distress. A fiscal distress episode is identified as a period when government experiences extreme funding difficulties. Most EWSs employ a specific set of only fiscal leading indicators predetermined by the researchers, which casts doubt on their robustness. We revisit this issue using extreme bounds analysis, which allows identifying robust leading indicators of fiscal distress from a large set. A robust leading indicator’s effect does not strongly depend on the model specification. Consistent with the theoretical predictions of latest generation crisis models, we find that both fiscal and non-fiscal leading indicators are robust. In addition, we find that a fiscal vulnerability indicator based on fiscal and non-fiscal leading indicators offers a 29% gain in predictive power compared to a traditional one based only on fiscal leading indicators. This suggests that both fiscal and non-fiscal leading indicators should be taken into account when assessing country’s vulnerability to fiscal distress.

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  • Martin Bruns & Tigran Poghosyan, 2018. "Leading indicators of fiscal distress: evidence from extreme bounds analysis," Applied Economics, Taylor & Francis Journals, vol. 50(13), pages 1454-1478, March.
    • Handle: RePEc:taf:applec:v:50:y:2018:i:13:p:1454-1478
    DOI: 10.1080/00036846.2017.1366639
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      • Mrs. Kerstin Gerling & Mr. Paulo A Medas & Mr. Tigran Poghosyan & Juan Farah-Yacoub & Yizhi Xu, 2017. "Fiscal Crises," IMF Working Papers 2017/086, International Monetary Fund.
    2. Honda, Jiro & Tapsoba, René & Issifou, Ismael, 2022. "When do we repair the roof? Insights from responses to fiscal crisis early warning signals," International Economics, Elsevier, vol. 172(C), pages 349-367.
    3. Blaise Gnimassoun & Isabelle Do Santos, 2021. "Robust structural determinants of public deficits in developing countries," Applied Economics, Taylor & Francis Journals, vol. 53(9), pages 1052-1076, February.
    4. Filip Bašić & Tomislav Globan, 2023. "Early bird catches the worm: finding the most effective early warning indicators of recessions," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 36(1), pages 2120040-212, December.
    5. Moreno Badia, Marialuz & Medas, Paulo & Gupta, Pranav & Xiang, Yuan, 2022. "Debt is not free," Journal of International Money and Finance, Elsevier, vol. 127(C).
    6. Tjeerd M. Boonman & Andrea E. Sanchez Urbina, 2020. "Extreme Bounds Analysis in Early Warning Systems for Currency Crises," Open Economies Review, Springer, vol. 31(2), pages 431-470, April.
    7. Barbara Jarmulska, 2022. "Random forest versus logit models: Which offers better early warning of fiscal stress?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 455-490, April.
    8. Stéphanie Pamies Sumner & Katia Berti, 2017. "A Complementary Tool to Monitor Fiscal Stress in European Economies," European Economy - Discussion Papers 049, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    9. Ms. Svetlana Cerovic & Mrs. Kerstin Gerling & Andrew Hodge & Mr. Paulo A Medas, 2018. "Predicting Fiscal Crises," IMF Working Papers 2018/181, International Monetary Fund.
    10. Liu, Ailan & Wang, Zhixuan & Wang, Ping, 2024. "Official or unofficial? extreme bounds analysis on the determinants of sovereign default," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    11. Rho, Caterina & Saenz, Manrique, 2021. "Financial stress and the probability of sovereign default," Journal of International Money and Finance, Elsevier, vol. 110(C).

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