Konferenzbeitrag
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 articial neural networks are able to predict the nominal interest rate better than linear and nonlinear Taylor 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 between 1987-2012 is nonlinear.
- Sprache
-
Englisch
- Erschienen in
-
Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2019: 30 Jahre Mauerfall - Demokratie und Marktwirtschaft - Session: Econometrics - Forecasting I ; No. B07-V3
- Klassifikation
-
Wirtschaft
Neural Networks and Related Topics
Forecasting Models; Simulation Methods
Money and Interest Rates: Forecasting and Simulation: Models and Applications
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Hinterlang, Natascha
- Ereignis
-
Veröffentlichung
- (wer)
-
ZBW - Leibniz-Informationszentrum Wirtschaft
- (wo)
-
Kiel, Hamburg
- (wann)
-
2019
- Handle
- Letzte Aktualisierung
- 10.03.2025, 11:41 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
- Konferenzbeitrag
Beteiligte
- Hinterlang, Natascha
- ZBW - Leibniz-Informationszentrum Wirtschaft
Entstanden
- 2019
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