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 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

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Year of publication:	2020
Authors:	Hinterlang, Natascha
Publisher:	[2020]: [S.l.] : SSRN
Subject:	Neuronale Netze \| Neural networks \| Geldpolitik \| Monetary policy \| Prognoseverfahren \| Forecasting model \| Theorie \| Theory

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