Predicting monetary policy using artificial neural networks

Natascha Hinterlang

No 44/2020, Discussion Papers from Deutsche Bundesbank

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.

Keywords: Forecasting; Monetary Policy; Artificial Neural Network; Taylor Rule; Reaction Function (search for similar items in EconPapers)
JEL-codes: C45 E47 E52 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-mac and nep-mon
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:bubdps:442020

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