Classification of monetary and fiscal dominance regimes using machine learning techniques
Natascha Hinterlang and
No 160, IMFS Working Paper Series from Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS)
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.
Keywords: Monetary-fiscal interaction; Machine Learning; Classification; Markov-switching DSGE (search for similar items in EconPapers)
JEL-codes: C38 E31 E63 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp, nep-mac, nep-mon and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:zbw:imfswp:160
Access Statistics for this paper
More papers in IMFS Working Paper Series from Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().