All models are wrong but all can be useful: Robust policy design using prediction pools

Szabolcs Deák, Paul Levine, Afrasiab Mirza and Joseph Pearlman

Journal of Economic Dynamics and Control, 2025, vol. 176, issue C

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.

Keywords: Bayesian estimation; DSGE models; Financial frictions; Forecasting; Prediction pools; Optimal simple rules (search for similar items in EconPapers)
JEL-codes: D52 D53 E44 G18 G23 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:176:y:2025:i:c:s0165188925000624

DOI: 10.1016/j.jedc.2025.105096

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