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Whose Inflation Rates Matter Most? A DSGE Model and Machine Learning Approach to Monetary Policy in the Euro Area
In the euro area, monetary policy is conducted by a single central bank for 20 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...
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| Published in: | MAGKS - Joint Discussion Paper Series in Economics (Band 32-2022) |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
Philipps-Universität Marburg
2022
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| Subjects: | |
| Online Access: | PDF Full Text |
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| Summary: | In the euro area, monetary policy is conducted by a single central bank for 20 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. We use this algorithm to (i) generally classify historical EMU data, and to (ii) determine the exact weight on the inflation rate of EMU members in each quarter of the past two decades. Our findings suggest disproportional emphasis of the ECB on the inflation rates of EMU members that exhibited high inflation rate volatility for the vast majority of the time frame considered (80%), with a median inflation weight of 67% on these countries. We show that these results stem from a tendency of the ECB to react more strongly to countries whose inflation rates exhibit greater deviations from their long-term trend. |
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| Physical Description: | 42 Pages |
| ISSN: | 1867-3678 |
| DOI: | 10.17192/es2024.0739 |