Arbeitspapier
Classification of monetary and fiscal dominance regimes using machine learning techniques
This paper identiftes U.S. monetary and ftscal dominance regimes using machine learning techniques. The algorithms are trained and verifted 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 classifter yields the best results. We ftnd clear evidence of ftscal dominance before Volcker. Monetary dominance is detected between 1984-1988, before a ftscally 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 ftnancial crisis is mixed with a tendency towards ftscal dominance.
- Language
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Englisch
- Bibliographic citation
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Series: IMFS Working Paper Series ; No. 160
- Classification
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Wirtschaft
Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
Price Level; Inflation; Deflation
Comparative or Joint Analysis of Fiscal and Monetary Policy; Stabilization; Treasury Policy
- Subject
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Monetary-fiscal interaction
Machine Learning
Classification
Markov-switching DSGE
- Event
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Geistige Schöpfung
- (who)
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Hinterlang, Natascha
Hollmayr, Josef
- Event
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Veröffentlichung
- (who)
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Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS)
- (where)
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Frankfurt a. M.
- (when)
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2021
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Hinterlang, Natascha
- Hollmayr, Josef
- Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS)
Time of origin
- 2021
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