Designing Robust Monetary Policy Using Prediction Pools
Szabolcs Deak (),
Paul Levine (),
Afrasiab Mirza and
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Afrasiab Mirza: University of Birmingham
Joseph Pearlman: City University
No 1219, School of Economics Discussion Papers from School of Economics, University of Surrey
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 maker first 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 problem 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 as in 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 of financial frictions.
JEL-codes: D52 D53 E44 G18 G23 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-dge, nep-for, nep-mac and nep-mon
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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:sur:surrec:1219
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