Monetary Policy under Uncertainty in Micro-Founded Macroeconometric Models
Noah Williams (),
Andrew Levin () and
No 478, Computing in Economics and Finance 2005 from Society for Computational Economics
Over the past decade there has been remarkable progress in developing empirical micro-founded macroeconomic models for monetary policy analysis that feature coherence both to economic theory and to the data. In this paper, we estimate using Bayesian methods a second-generation micro founded model of the U.S. economy. We examine the characteristics of optimal monetary policies in the model, where we consider two types of policy objectives: general stabilization of macroeconomic fluctuations associated with "flexible inflation targeting," and maximization of consumer welfare. We find that, for either type of objective, optimized versions of the Taylor Rule are able to deliver outcomes that are only a few percent below the first-best policy. We then explore optimal policy taking account of parameter and specification uncertainty and identify the parameters and specification issues that are critical for the conduct of monetary policy and hence have the highest value in terms of research
JEL-codes: E (search for similar items in EconPapers)
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Chapter: Monetary Policy under Uncertainty in Micro-Founded Macroeconometric Models (2006)
Working Paper: Monetary policy under uncertainty in micro-founded macroeconometric models (2005)
Working Paper: Monetary Policy Under Uncertainty in Micro-Founded Macroeconometric Models (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf5:478
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