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 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 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.
- Designing Robust Monetary Policy Using Prediction Pools
- Szabolcs Deak (Author) - University of SurreyPaul Leslie Levine (Corresponding Author) - University of Surrey, EconomicsAfrasiab Mirza (Author) - University of BirminghamJoseph Pearlman (Author) - City, University of London
- Discussion Papers in Economics
- Robust Policy, ES/K005154/1, Economic and Social Research Council (United Kingdom, Swindon) - ESRC
- We acknowledge financial support from the ESRC, grant reference ES/K005154/1 and from the EU Framework Programme 7, grant agreement number 612796
- 991154294002346
- Economics
- English
- Working paper