Predicting monetary policy using artificial neural networks

Arbeitspapier

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.

Object Details

• ISBN: 978-3-95729-752-5
• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Deutsche Bundesbank Discussion Paper ; No. 44/2020

Classification and Topics

• Classification: Wirtschaft ; Neural Networks and Related Topics ; Money and Interest Rates: Forecasting and Simulation: Models and Applications ; Monetary Policy
• Subject: Forecasting ; Monetary Policy ; Artificial Neural Network ; Taylor Rule ; Reaction Function

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Hinterlang, Natascha
• Event: Veröffentlichung
• (who): Deutsche Bundesbank
• (where): Frankfurt a. M.
• (when): 2020

Further information

• Handle: http://hdl.handle.net/10419/222663
• Last update: 10.03.2025, 11:44 AM CET