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

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.

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File URL: http://webpages.ursinus.edu/egaus/Research/IJMMNO_BAYES.pdf
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Paper provided by Ursinus College, Department of Economics in its series Working Papers with number 14-01.

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Length: pages
Date of creation: 12 Jul 2012
Date of revision: 14 Dec 2013
Publication status: Published
Handle: RePEc:urs:urswps:14-01
Contact details of provider: Postal: Ursinus College 601 East Main St. Collegeville, PA 19426
Web page: http://webpages.ursinus.edu/ecba/

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  1. Bennett T. McCallum, 1981. "On Non-Uniqueness in Rational Expectations Models: An Attempt at Perspective," NBER Working Papers 0684, National Bureau of Economic Research, Inc.
  2. Eric Gaus, 2012. "Robust Stability of Monetary Policy Rules under Adaptive Learning," Working Papers 13-01, Ursinus College, Department of Economics, revised 14 Dec 2012.
  3. John Duffy & Wei Xiao, 2007. "The Value of Interest Rate Stabilization Policies When Agents Are Learning," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(8), pages 2041-2056, December.
  4. James Bullard & Kaushik Mitra, 2002. "Learning about monetary policy rules," Working Papers 2000-001, Federal Reserve Bank of St. Louis.
  5. Fabio Milani, 2005. "Expectations, Learning and Macroeconomic Persistence," Macroeconomics 0510022, EconWPA.
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