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

Author

Listed:
  • Daniel Stempel

    (University of Duesseldorf)

  • Johannes Zahner

    (University of Marburg)

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.

Suggested Citation

  • Daniel Stempel & Johannes Zahner, 2022. "DSGE Models and Machine Learning: An Application to Monetary Policy in the Euro Area," MAGKS Papers on Economics 202232, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    • Handle: RePEc:mar:magkse:202232
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    References listed on IDEAS

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    1. Matthew Canzoneri & Robert Cumby & Behzad Diba & Olena Mykhaylova, 2006. "New Keynesian Explanations of Cyclical Movements in Aggregate Inflation and Regional Inflation Differentials," Open Economies Review, Springer, vol. 17(1), pages 27-55, January.
    2. Livia Paranhos, 2021. "Predicting Inflation with Recurrent Neural Networks," Papers 2104.03757, arXiv.org, revised Oct 2023.
    3. Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
    4. Jing Cynthia Wu & Fan Dora Xia, 2020. "Negative interest rate policy and the yield curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 653-672, September.
    5. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    6. Dimitris Papageorgiou, 2014. "BoGGEM: a dynamic stochastic general equilibrium model for policy simulations," Working Papers 182, Bank of Greece.
    7. Jeremy Fouliard & Michael Howell & Hélène Rey & Vania Stavrakeva, 2020. "Answering the Queen: Machine Learning and Financial Crises," NBER Working Papers 28302, National Bureau of Economic Research, Inc.
    8. Fritz Breuss & Katrin Rabitsch, 2009. "An estimated two-country DSGE model of Austria and the Euro Area," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 36(1), pages 123-158, February.
    9. Garcia, Pablo & Jacquinot, Pascal & Lenarčič, Črt & Lozej, Matija & Mavromatis, Kostas, 2023. "Global models for a global pandemic: The impact of COVID-19 on small euro area economies," Journal of Macroeconomics, Elsevier, vol. 77(C).
    10. Pau Rabanal, 2009. "Inflation Differentials between Spain and the EMU: A DSGE Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(6), pages 1141-1166, September.
    11. Duarte, Margarida & Wolman, Alexander L., 2008. "Fiscal policy and regional inflation in a currency union," Journal of International Economics, Elsevier, vol. 74(2), pages 384-401, March.
    12. Fritz Breuss & Katrin Rabitsch, 2008. "An Estimated Two-Country DSGE Model of Austria and the Euro Area. FIW Working Paper," WIFO Studies, WIFO, number 60008, Juni.
    13. Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.
    14. Hinterlang, Natascha, 2020. "Predicting monetary policy using artificial neural networks," Discussion Papers 44/2020, Deutsche Bundesbank.
    15. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
    16. Consolo, Agostino & Koester, Gerrit & Nickel, Christiane & Porqueddu, Mario & Smets, Frank, 2021. "The need for an inflation buffer in the ECB’s price stability objective – the role of nominal rigidities and inflation differentials," Occasional Paper Series 279, European Central Bank.
    17. James D. Hamilton, 2018. "Why You Should Never Use the Hodrick-Prescott Filter," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 831-843, December.
    18. Michael Ehrmann & Marcel Fratzscher, 2007. "Communication by Central Bank Committee Members: Different Strategies, Same Effectiveness?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(2‐3), pages 509-541, March.
    19. Paranhos, Livia, 2021. "Predicting Inflation with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1344, University of Warwick, Department of Economics.
    20. Neyer, Ulrike & Stempel, Daniel, 2022. "How should central banks react to household inflation heterogeneity?," DICE Discussion Papers 378, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    21. Angeloni Ignazio & Ehrmann Michael, 2007. "Euro Area Inflation Differentials," The B.E. Journal of Macroeconomics, De Gruyter, vol. 7(1), pages 1-36, August.
    22. Vanda Almeida, 2009. "Bayesian estimation of a DSGE model for the Portuguese economy," Working Papers w200914, Banco de Portugal, Economics and Research Department.
    23. Andrés, Javier & Ortega, Eva & Vallés, Javier, 2008. "Competition and inflation differentials in EMU," Journal of Economic Dynamics and Control, Elsevier, vol. 32(3), pages 848-874, March.
    24. Mr. Andrew J Tiffin, 2019. "Machine Learning and Causality: The Impact of Financial Crises on Growth," IMF Working Papers 2019/228, International Monetary Fund.
    25. Hinterlang, Natascha & Hollmayr, Josef, 2020. "Classification of monetary and fiscal dominance regimes using machine learning techniques," Discussion Papers 51/2020, Deutsche Bundesbank.
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    More about this item

    Keywords

    New Keynesian Models; Monetary Policy; European Monetary Union; Neural Networks; Transfer Learning;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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