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Modelling Early Warning System for Debt Rescheduling in ASEAN Countries

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  • Sapwarobol, Teerasak

    (Faculty of Economics, Kasetsart University, Thailand)

Abstract

This study aims to develop an early warning system for debt rescheduling in ASEAN countries by utilizing yearly time series data from 1999 to 2019. The logit model is employed to construct the early warning system for debt rescheduling in ASEAN countries, with debt rescheduled data collected from The World Bank’s International Debt Statistics database. The empirical results indicate that the early warning system model for debt rescheduling in ASEAN countries should comprise four variables: the unemployment rate, concessional debt to total debt, external debt over GDP, and international reserve to short-term debt. Interestingly, when setting the cutoff value at 0.5, the model demonstrates high predictive accuracy, with a Type II error rate of 10 percent and a Type I error rate of only 4.1 percent. Overall, the early warning system model for debt rescheduling in ASEAN countries appears capable of correctly predicting events 80 times out of 84.

Suggested Citation

  • Sapwarobol, Teerasak, 2024. "Modelling Early Warning System for Debt Rescheduling in ASEAN Countries," Asian Journal of Applied Economics/ Applied Economics Journal, Kasetsart University, Faculty of Economics, Center for Applied Economic Research, vol. 31(1), pages 133-151, January-J.
    • Handle: RePEc:ris:apecjn:0098
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    Keywords

    ASEAN countries; debt rescheduling; early warning system;
    All these keywords.

    JEL classification:

    • F34 - International Economics - - International Finance - - - International Lending and Debt Problems
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • H63 - Public Economics - - National Budget, Deficit, and Debt - - - Debt; Debt Management; Sovereign Debt

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