Machine Learning and Central Banks: Ready for Prime Time?

Abstract

We review what machine learning (ML) 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 ML, we propose the use of vector autoregression (VAR) methods in central banks to speculate how ML 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, and we believe that the same must be the case for ML.