Machine Learning and Central Banks: Ready for Prime Time?

Hans Genberg and Ozer Karagedikli

Working Papers from South East Asian Central Banks (SEACEN) Research and Training Centre

Abstract: In this article we review what machine learning might have to offer central banks as an analytical approach to support monetary policy decisions. After describing the central bank’s “problem†and providing a brief introduction to machine learning, we propose to use the gradual adoption of Vector Auto Regression (VAR) methods in central banks to speculate how machine learning models must (will?) evolve to become influential analytical tools supporting central banks’ monetary policy decisions. We argue that VAR methods achieved that status only after they incorporated elements that allowed users to interpret them in terms of structural economic theories. We believe that the same has to be the case for machine learning model.

JEL-codes: J31 J64 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2021-03
New Economics Papers: this item is included in nep-big, nep-cba, nep-cmp, nep-fmk, nep-mon and nep-sea
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:sea:wpaper:wp43

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