Optimal monetary policy using reinforcement learning

Natascha Hinterlang and Alina Tänzer

No 51/2021, Discussion Papers from Deutsche Bundesbank

Abstract: 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.

Keywords: Optimal Monetary Policy; Reinforcement Learning; Artificial Neural Network; Machine Learning; Reaction Function (search for similar items in EconPapers)
JEL-codes: C45 C61 E52 E58 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cba, nep-cmp, nep-cwa, nep-dge, nep-his, nep-mac and nep-mon
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:bubdps:512021

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