Arbeitspapier
A Critical Note on the Forecast Error Variance Decomposition
The paper questions the reasonability of using forecast error variance decompositions for assessing the role of different structural shocks in business cycle fluctuations. It is shown that the forecast error variance decomposition is related to a dubious definition of the business cycle. A historical variance decomposition approach is proposed to overcome the problems related to the forecast error variance decomposition.
- Language
-
Englisch
- Bibliographic citation
-
Series: ZEW Discussion Papers ; No. 08-065
- Classification
-
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Business Fluctuations; Cycles
- Subject
-
Business Cycles
Structural Vector Autoregression Models
Forecast Error Variance Decomposition
Historical Variance Decomposition
Konjunkturprognose
Prognoseverfahren
Statistischer Fehler
Varianzanalyse
Dekompositionsverfahren
VAR-Modell
Theorie
HVD
- Event
-
Geistige Schöpfung
- (who)
-
Seymen, Atilim
- Event
-
Veröffentlichung
- (who)
-
Zentrum für Europäische Wirtschaftsforschung (ZEW)
- (where)
-
Mannheim
- (when)
-
2008
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Seymen, Atilim
- Zentrum für Europäische Wirtschaftsforschung (ZEW)
Time of origin
- 2008
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A critical note on the forecast error variance decomposition
SVARs identification through bounds on the forecast error variance
Identifying Proxy VARs with Restrictions on the Forecast Error Variance
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Regression Calibration with Heteroscedastic Error Variance
When does information on forecast variance improve the performance of a combined forecast?
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