Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/222663 
Year of Publication: 
2020
Series/Report no.: 
Deutsche Bundesbank Discussion Paper No. 44/2020
Publisher: 
Deutsche Bundesbank, Frankfurt a. M.
Abstract: 
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.
Subjects: 
Forecasting
Monetary Policy
Artificial Neural Network
Taylor Rule
Reaction Function
JEL: 
C45
E47
E52
ISBN: 
978-3-95729-752-5
Document Type: 
Working Paper

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