Working Papers

(WP1/2021) Machine Learning and Central Banks: Ready for Prime Time?

Publish Date: : March 2021
Authors:: Özer Karagedikli and Hans Genberg


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.