Predicting Monetary Policy Using Artificial Neural Networks

Natascha Hinterlang

VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy from Verein für Socialpolitik / German Economic Association

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-codes: C45 C53 E47 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-big, nep-cba, nep-cmp, nep-for, nep-mac and nep-mon
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:vfsc19:203503

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