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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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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
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  1. Kaushik Mitra & James Bullard, . "Learning About Monetary Policy Rules," Discussion Papers 00/41, Department of Economics, University of York.
  2. John Duffy & Wei Xiao, 2006. "The Value of Interest Rate Stabilization Policies When Agents are Learning," Working Papers 284, University of Pittsburgh, Department of Economics, revised Oct 2006.
  3. McCallum, Bennett T., 1983. "On non-uniqueness in rational expectations models : An attempt at perspective," Journal of Monetary Economics, Elsevier, vol. 11(2), pages 139-168.
  4. Fabio Milani, 2005. "Expectations, Learning and Macroeconomic Persistence," Working Papers 050608, University of California-Irvine, Department of Economics.
  5. Eric Gaus, 2013. "Robust Stability of Monetary Policy Rules under Adaptive Learning," Southern Economic Journal, Southern Economic Association, vol. 80(2), pages 439-453, October.
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