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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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    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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