Monetary Policy In An Uncertain World: Probability Models And The Design Of Robust Monetary Rules

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Executive Summary

The past forty years or so has seen a remarkable transformation in macro-models used by central banks, policymakers and forecasting bodies. This paper describes this transformation from reduced-form behavioral equations estimated separately, through to contemporary micro-founded Dynamic Stochastic General Equilibrium (DSGE) models estimated by systems methods. In particular by treating DSGE models estimated by Bayesian-Maximum-Likelihood methods I argue that they can be considered as probability models in the sense de-scribed by Sims (2007) and be used for risk-assessment and policy design.

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