Machine Learning of Optimal Interest Rate Policy

Machine Learning of Optimal Interest Rate Policy

Client/Allowance

Deutsche Bundesbank

Period: 01.04.2018 – 31.03.2019

In this research project we aim to algorithmically determine the optimal interest rate policy of central banks by means of realistic model-based simulations of the economy. To carry out these simulations, the macroeconomic model by Riedler (2017), developed at the ZEW, is extended by a realistic modelling of the heterogeneous consumption and saving behavior of households. Employing modern methods of machine learning, we algorithmically determine the “optimal” interest rate policy: During a great number of simulation runs, the central bank “learns“ the respective optimal interest setting strategy for reaching a certain target figure. This approach allows to realistically evaluate the proposals for an optimal interest rate policy that are currently being discussed in the academic and the public sphere, such as the “Taylor Rule”.

Project members

Jesper Riedler

Jesper Riedler

Project Coordinator
Advanced Researcher

To the profile
Client/Allowance
Deutsche Bundesbank, Frankfurt am Main, DE
Cooperation partner
Deutsche Bundesbank, Frankfurt am Main, DE