DSGE Models and Machine Learning: An Application to Monetary Policy in the Euro Area

Daniel Stempel () and Johannes Zahner ()
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Daniel Stempel: University of Duesseldorf
Johannes Zahner: University of Marburg

MAGKS Papers on Economics from Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung)

Abstract: In the euro area, monetary policy is conducted by a single central bank for 19 member countries. However, countries are heterogeneous in their economic development, including their inflation rates. This paper combines a New Keynesian model and a neural network to assess whether the European Central Bank (ECB) conducted monetary policy between 2002 and 2022 according to the weighted average of the inflation rates within the European Monetary Union (EMU) or reacted more strongly to the inflation rate developments of certain EMU countries. The New Keynesian model first generates data which is used to train and evaluate several machine learning algorithms. We find that a neural network performs best out-of-sample. Thus, we use this algorithm to classify historical EMU data. Our findings suggest disproportional emphasis on the inflation rates experienced by southern EMU members for the vast majority of the time frame considered (80%). We argue that this result stems from a tendency of the ECB to react more strongly to countries whose inflation rates exhibit greater deviations from their long-term trend.

Keywords: New Keynesian Models; Monetary Policy; European Monetary Union; Neural Networks; Transfer Learning (search for similar items in EconPapers)
JEL-codes: C45 C53 E58 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2022
New Economics Papers: this item is included in nep-ban, nep-big, nep-cba, nep-cmp, nep-dge, nep-eec and nep-mon
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Persistent link: https://EconPapers.repec.org/RePEc:mar:magkse:202232

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