Evaluation of International Monetary Policy Coordination: Evidence from Machine Learning Algorithms
Ufuk Can (),
Omur Saltik (),
Zeynep Gizem Can () and
Suleyman Degirmen ()
Additional contact information
Ufuk Can: Central Bank of the Republic of Türkiye
Omur Saltik: Economics Research Department of Marbaş
Zeynep Gizem Can: Adana Alparslan Türkeş Science and Technology University
Suleyman Degirmen: Mersin University
Computational Economics, 2025, vol. 65, issue 5, No 1, 2476 pages
Abstract:
Abstract Being a critical for integrating of the global financial system, international monetary policy coordination deserves attention to offer timely and vital information about the ongoing interaction between monetary policy practices and its future. This paper reveals the integration of economies in international monetary policy coordination and the underlying adjustment dynamics of this process in advanced and emerging economies. We perform convolutional signal analysis, K-means clustering, and classification algorithms to extract similar features or patterns of monetary policy actions implemented in different economies. The empirical results show that emerging economies are more vulnerable to internal and external shocks than advanced economies due to their financial vulnerabilities. Advanced and emerging economies respond similarly but differently within their clusters. Global monetary conditions are the key driver of monetary policy decisions; however, output growth, international reserves, policy rate, credit default swap, and consumer price index are the other important country-specific determinants. This study enhances our comprehension of the intricate dynamics within monetary policy interactions among advanced and emerging economies, shedding light on the nuanced dynamics of leader–follower relationships. Additionally, this paper not only extends the literature on international monetary policy coordination by using a state-of-the-art dataset and analysis, but also provides a solid foundation for future research on this topic.
Keywords: International monetary policy coordination; Convolutional signal analysis; K-means clustering; Classification (search for similar items in EconPapers)
JEL-codes: C63 E47 E52 E58 F42 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s10614-024-10643-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:65:y:2025:i:5:d:10.1007_s10614-024-10643-z
Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2
DOI: 10.1007/s10614-024-10643-z
Access Statistics for this article
Computational Economics is currently edited by Hans Amman
More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().