Arbeitspapier
Early warning system of government debt crises
The European debt crisis has revealed serious deficiencies and risks on a proper functioning of the monetary union. Against this backdrop, early warning systems are of crucial importance. In this study that focuses on euro area member states, the robustness of early warning systems to predict crises of government debt is evaluated. Robustness is captured via several dimensions, such as the chronology of past crises, econometric methods, and the selection of indicators in forecast combinations. The chosen approach is shown to be crucial for the results. Therefore, the construction of early warning systems should be based on a wide set of variables and methods in order to be able to draw reliable conclusions.
- Sprache
-
Englisch
- Erschienen in
-
Series: DIW Discussion Papers ; No. 1724
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
National Debt; Debt Management; Sovereign Debt
- Thema
-
sovereign debt crises
multiple bubbles
signal approach
logit
panel data model
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Dreger, Christian
Kholodilin, Konstantin A.
- Ereignis
-
Veröffentlichung
- (wer)
-
Deutsches Institut für Wirtschaftsforschung (DIW)
- (wo)
-
Berlin
- (wann)
-
2018
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 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.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Dreger, Christian
- Kholodilin, Konstantin A.
- Deutsches Institut für Wirtschaftsforschung (DIW)
Entstanden
- 2018
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