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• W2948090849 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." @default.
• W2948090849 created "2019-06-14" @default.
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• W2948090849 date "2019-01-01" @default.
• W2948090849 modified "2023-09-27" @default.
• W2948090849 title "Designing Robust Monetary Policy Using Prediction Pools" @default.
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• W2948090849 hasPublicationYear "2019" @default.
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