Arbeitspapier
Bayesian methods for dynamic multivariate models
If multivariate dynamic models are to be used to guide decision-making, it is important that it be possible to provide probability assessments of their results. Bayesian VAR models in the existing literature have not commonly (in fact, not at all as far as we know) been presented with error bands around forecasts or policy projections based on the posterior distribution. In this paper we show that it is possible to introduce prior information in both reduced form and structural VAR models without introducing substantial new computational burdens. With our approach, identified VAR analysis of large systems (e.g., 20-variable models) becomes possible.
- Language
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Englisch
- Bibliographic citation
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Series: Working Paper ; No. 96-13
- Classification
-
Wirtschaft
- Subject
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Econometric models
Forecasting
- Event
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Geistige Schöpfung
- (who)
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Sims, Christopher A.
Zha, Tao
- Event
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Veröffentlichung
- (who)
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Federal Reserve Bank of Atlanta
- (where)
-
Atlanta, GA
- (when)
-
1996
- Handle
- Last update
-
10.03.2025, 11:44 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
- Sims, Christopher A.
- Zha, Tao
- Federal Reserve Bank of Atlanta
Time of origin
- 1996
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