Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/234736
Authors: 
Hinterlang, Natascha
Hollmayr, Josef
Year of Publication: 
2021
Series/Report no.: 
IMFS Working Paper Series No. 160
Publisher: 
Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), Frankfurt a. M.
Abstract: 
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.
Subjects: 
Monetary-fiscal interaction
Machine Learning
Classification
Markov-switching DSGE
JEL: 
C38
E31
E63
Document Type: 
Working Paper

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