City Research Online

Abstract

We study the design of monetary policy rules robust to model uncertainty using a novel methodology. In our application, policymakers choose the optimal rule by attaching weights to a set of well-established DSGE models with varied financial frictions. The novelty of our methodology is to compute each model's weight based on their relative forecasting performance. Our results highlight the superiority of predictive pools over Bayesian model averaging and the need to combine models when none can be deemed as the true data generating process. In addition, we find that the optimal across-model robust policy rule exhibits attenuation, and nests a price level rule which has good robustness properties. Therefore, the application of our methodology offers a new rationale for price-level rules, namely the presence of uncertainty over the nature of financial frictions.

Publication Type:	Article
Additional Information:	© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Publisher Keywords:	Bayesian estimation, DSGE models, Financial frictions, Forecasting, Prediction pools, Optimal simple rules
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HF Commerce H Social Sciences > HJ Public Finance
Departments:	School of Policy & Global Affairs School of Policy & Global Affairs > Economics
SWORD Depositor:	Symplectic Administrator

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