Designing Robust Monetary Policy Using Prediction Pools

Szabolcs Deak, Paul Levine (), A. Mirza and Joseph Pearlman

Working Papers from Department of Economics, City University London

Abstract: How should a forward-looking policy maker conduct monetary policy when she has a finite set of models at her disposal, none of which are believed to be the true data generating process? In our approach, the policy makerfirst assigns weights to models based on relative forecasting performance rather than in-sample fit, consistent with her forward-looking objective. These weights are then used to solve a policy design prob-lem that selects the optimized Taylor-type interest-rate rule that is robust to model uncertainty across a set of well-established DSGE models with and without financial frictions. We find that the choice of weights has a significant impact on the robust optimized rule which is more inertial and aggressive than either the non-robust single model counterparts or the optimal robust rule based on backward-looking weights asin the common alternative Bayesian Model Averaging. Importantly, we show that a price-level rule has excellent welfare and robustness properties, and therefore should be viewed as a key instrument for policy makers facing uncertainty over the nature offinancial frictions.

JEL-codes: H (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-cba, nep-dge, nep-for, nep-mon and nep-ore
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Working Paper: Designing Robust Monetary Policy Using Prediction Pools (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:cty:dpaper:19/11

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