Arbeitspapier

Early warning models for systemic banking crises: Can political indica-tors improve prediction?

This study provides the first attempt to evaluate whether a logit early warning system (EWS) for systemic banking crises can produce better predictions when political indicators are used alongside traditional macro-financial indicators. Based on a dataset covering 32 advanced economies for the period 1975-2017, we show that the inclusion of political indicators helps improve the predictive performance of the model. While the improvement is small, it is statistically significant and consistent for several different performance measures and robustness tests. Among the newly employed political variables, variables indicating the political ideology of the ruling party and the time in office of the incumbent chief executive show significant correlations with the likelihood of systemic banking crises. The results suggest that a systemic banking crisis is less likely when the government is left-wing and when the chief executive officer has been in office longer.

Sprache
Englisch

Erschienen in
Series: Jena Economic Research Papers ; No. 2022-007

Klassifikation
Wirtschaft
Multiple or Simultaneous Equation Models: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
Forecasting Models; Simulation Methods
Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook: General
International Finance Forecasting and Simulation: Models and Applications
Financial Crises
Financial Institutions and Services: Government Policy and Regulation
Thema
early warning systems
systemic banking crises
vulnerability
political indicators
macro-financial indicators

Ereignis
Geistige Schöpfung
(wer)
Tran Huynh
Übelmesser, Silke
Ereignis
Veröffentlichung
(wer)
Friedrich Schiller University Jena, Faculty of Economics and Business Administration
(wo)
Jena
(wann)
2022

Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Tran Huynh
  • Übelmesser, Silke
  • Friedrich Schiller University Jena, Faculty of Economics and Business Administration

Entstanden

  • 2022

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