Learning and Loss Functions: Comparing Optimal and Operational Monetary Policy Rules

Eric Gaus and Srikanth Ramamurthy

Working Papers from Ursinus College, Department of Economics

Abstract: Modern Bayesian tools aided by MCMC techniques allow researchers to estimate models with increasingly intricate dynamics. This paper highlights the application of these tools with an empirical assessment of optimal versus operational monetary policy rules within a standard New Keynesian macroeconomic model with adaptive learning. The question of interest is which of the two policy rules - contemporaneous data or expectations of current variables - better describes the policy undertaken by the U.S. central bank. Results for the data period 1954:III to 2007:I indicate that the data strongly favors contemporaneous expectations over real time data.

Keywords: Adaptive Learning; Rational Expectations; Bayesian Econometrics; MCMC (search for similar items in EconPapers)
JEL-codes: D83 E52 (search for similar items in EconPapers)
Pages: pages
Date: 2012-07-12, Revised 2013-12-14
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Persistent link: https://EconPapers.repec.org/RePEc:urs:urswps:14-01

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