Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/203503
Authors: 
Hinterlang, Natascha
Year of Publication: 
2019
Series/Report no.: 
Beiträge zur Jahrestagung des Vereins für Socialpolitik 2019: 30 Jahre Mauerfall - Demokratie und Marktwirtschaft - Session: Econometrics - Forecasting I No. B07-V3
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 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.
JEL: 
C45
C53
E47
Document Type: 
Conference Paper

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