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Classification of monetary and fiscal dominance regimes using machine learning techniques

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  • Hinterlang, Natascha
  • Hollmayr, Josef

Abstract

This paper identifies U.S. monetary and fiscal dominance regimes using machine learning techniques. The algorithms are trained and verified by employing simulated data from Markov-switching DSGE models, before they classify regimes from 1968-2017 using actual U.S. data. All machine learning methods outperform a standard logistic regression concerning the simulated data. Among those the Boosted Ensemble Trees classifier yields the best results. We find clear evidence of fiscal dominance before Volcker. Monetary dominance is detected between 1984-1988, before a fiscally led regime turns up around the stock market crash lasting until 1994. Until the beginning of the new century, monetary dominance is established, while the more recent evidence following the financial crisis is mixed with a tendency towards fiscal dominance.

Suggested Citation

  • Hinterlang, Natascha & Hollmayr, Josef, 2020. "Classification of monetary and fiscal dominance regimes using machine learning techniques," Discussion Papers 51/2020, Deutsche Bundesbank.
  • Handle: RePEc:zbw:bubdps:512020
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    1. Francesco Bianchi & Leonardo Melosi, 2017. "Escaping the Great Recession," American Economic Review, American Economic Association, vol. 107(4), pages 1030-1058, April.
    2. Alessi, Lucia & Detken, Carsten, 2018. "Identifying excessive credit growth and leverage," Journal of Financial Stability, Elsevier, vol. 35(C), pages 215-225.
    3. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2018. "An evaluation of early warning models for systemic banking crises: Does machine learning improve predictions?," Discussion Papers 48/2018, Deutsche Bundesbank.
    4. Leeper, Eric M., 1991. "Equilibria under 'active' and 'passive' monetary and fiscal policies," Journal of Monetary Economics, Elsevier, vol. 27(1), pages 129-147, February.
    5. Thomas J. Sargent & Neil Wallace, 1984. "Some Unpleasant Monetarist Arithmetic," Palgrave Macmillan Books, in: Brian Griffiths & Geoffrey E. Wood (ed.), Monetarism in the United Kingdom, pages 15-41, Palgrave Macmillan.
    6. Francesco Bianchi & Cosmin Ilut, 2017. "Monetary/Fiscal Policy Mix and Agent's Beliefs," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 26, pages 113-139, October.
    7. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
    8. Carlo Favero & Tommaso Monacelli, 2005. "Fiscal Policy Rules and Regime (In)Stability: Evidence from the U.S," Working Papers 282, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. Serena Ng, 2014. "Viewpoint: Boosting Recessions," Canadian Journal of Economics, Canadian Economics Association, vol. 47(1), pages 1-34, February.
    10. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
    11. Martin Kliem & Alexander Kriwoluzky & Samad Sarferaz, 2016. "On the Low‐Frequency Relationship Between Public Deficits and Inflation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(3), pages 566-583, April.
    12. 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.
    13. Farmer, Roger E.A. & Waggoner, Daniel F. & Zha, Tao, 2009. "Understanding Markov-switching rational expectations models," Journal of Economic Theory, Elsevier, vol. 144(5), pages 1849-1867, September.
    14. Meltzer, Allan H., 2011. "Politics and the Fed," Journal of Monetary Economics, Elsevier, vol. 58(1), pages 39-48, January.
    15. Manuel Gonzalez‐Astudillo, 2018. "Identifying the Stance of Monetary Policy at the Zero Lower Bound: A Markov‐Switching Estimation Exploiting Monetary‐Fiscal Policy Interdependence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(1), pages 115-154, February.
    16. Moreno Badia, Marialuz & Medas, Paulo & Gupta, Pranav & Xiang, Yuan, 2022. "Debt is not free," Journal of International Money and Finance, Elsevier, vol. 127(C).
    17. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
    18. Traum, Nora & Yang, Shu-Chun S., 2011. "Monetary and fiscal policy interactions in the post-war U.S," European Economic Review, Elsevier, vol. 55(1), pages 140-164, January.
    19. Francesco Bianchi, 2012. "Evolving Monetary/Fiscal Policy Mix in the United States," American Economic Review, American Economic Association, vol. 102(3), pages 167-172, May.
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    2. 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).

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    More about this item

    Keywords

    Monetary-fiscal interaction; Machine Learning; Classification; Markov-switching DSGE;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E63 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Comparative or Joint Analysis of Fiscal and Monetary Policy; Stabilization; Treasury Policy

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