Classification of monetary and fiscal dominance regimes using machine learning techniques

Natascha Hinterlang and Josef Hollmayr

Journal of Macroeconomics, 2022, vol. 74, issue C

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.

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)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmacro:v:74:y:2022:i:c:s0164070422000623

DOI: 10.1016/j.jmacro.2022.103469

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