Arbeitspapier
Conditional forecasts in dynamic multivariate models
In the existing literature, conditional forecasts in the vector autoregressive (VAR) framework have not been commonly presented with probability distributions or error bands. This paper develops Bayesian methods for computing such distributions or bands. It broadens the class of conditional forecasts to which the methods can be applied. The methods work for both structural and reduced-form VAR models and, in contrast to common practices, account for the parameter uncertainty in small samples. Empirical examples under the flat prior and under the reference prior of Sims and Zha (1998) are provided to show the use of these methods.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Paper ; No. 98-22
- Klassifikation
-
Wirtschaft
- Thema
-
Econometric models
Forecasting
Time-series analysis
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Waggoner, Daniel F.
Zha, Tao
- Ereignis
-
Veröffentlichung
- (wer)
-
Federal Reserve Bank of Atlanta
- (wo)
-
Atlanta, GA
- (wann)
-
1998
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 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
- Waggoner, Daniel F.
- Zha, Tao
- Federal Reserve Bank of Atlanta
Entstanden
- 1998
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