Arbeitspapier
Predicting monetary policy using artificial neural networks
This paper analyses the forecasting performance of monetary policy reaction functions using U.S. Federal Reserve's Greenbook real-time data. The results indicate that artificial neural networks are able to predict the nominal interest rate better than linear and nonlinearTaylor rule models as well as univariate processes. While in-sample measures usually imply a forward-looking behaviour of the central bank, using nowcasts of the explanatory variables seems to be better suited for forecasting purposes. Overall, evidence suggests that U.S. monetary policy behaviour between1987-2012 is nonlinear.
- ISBN
-
978-3-95729-752-5
- Sprache
-
Englisch
- Erschienen in
-
Series: Deutsche Bundesbank Discussion Paper ; No. 44/2020
- Klassifikation
-
Wirtschaft
Neural Networks and Related Topics
Money and Interest Rates: Forecasting and Simulation: Models and Applications
Monetary Policy
- Thema
-
Forecasting
Monetary Policy
Artificial Neural Network
Taylor Rule
Reaction Function
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Hinterlang, Natascha
- Ereignis
-
Veröffentlichung
- (wer)
-
Deutsche Bundesbank
- (wo)
-
Frankfurt a. M.
- (wann)
-
2020
- 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.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Hinterlang, Natascha
- Deutsche Bundesbank
Entstanden
- 2020
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