Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/248736 
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
Deutsche Bundesbank Discussion Paper No. 51/2021
Verlag: 
Deutsche Bundesbank, Frankfurt a. M.
Zusammenfassung: 
This paper introduces a reinforcement learning based approach to compute optimal interest rate reaction functions in terms of fulfilling inflation and output gap targets. The method is generally flexible enough to incorporate restrictions like the zero lower bound, nonlinear economy structures or asymmetric preferences. We use quarterly U.S. data from1987:Q3-2007:Q2 to estimate (nonlinear) model transition equations, train optimal policies and perform counterfactual analyses to evaluate them, assuming that the transition equations remain unchanged. All of our resulting policy rules outperform other common rules as well as the actual federal funds rate. Given a neural network representation of the economy, our optimized nonlinear policy rules reduce the central bank's loss by over43 %. A DSGE model comparison exercise further indicates robustness of the optimized rules.
Schlagwörter: 
Optimal Monetary Policy
Reinforcement Learning
Artificial Neural Network
Machine Learning
Reaction Function
JEL: 
C45
C61
E52
E58
ISBN: 
978-3-95729-861-4
Dokumentart: 
Working Paper
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