TY  - UNPD
A1  - Hinterlang, Natascha
A1  - Hollmayr, Josef
T1  - Classification of monetary and fiscal dominance regimes using machine learning techniques
T2  - Working paper series / Institute for Monetary and Financial Stability ; 160
N2  - The authors identify 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. The authors 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.
T3  - Working paper series / Institute for Monetary and Financial Stability - 160 
KW  - Monetary-fiscal interaction
KW  - Machine Learning
KW  - Classification
KW  - Markov-switching DSGE
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/61771
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-617716
UR  - https://www.imfs-frankfurt.de/de/forschung/imfs-working-papers/details/publication/classification-of-monetary-and-fiscal-dominance-regimes-using-machine-learning-techniques.html
IS  - May 19, 2021
PB  - Johann Wolfgang Goethe-Univ., Inst. for Monetary and Financial Stability
CY  - Frankfurt am Main
ER  -