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

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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.

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File URL: http://webpages.ursinus.edu/egaus/Research/IJMMNO_BAYES.pdf
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Bibliographic Info

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

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Postal: Ursinus College 601 East Main St. Collegeville, PA 19426
Web page: http://webpages.ursinus.edu/ecba/
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Related research

Keywords: Adaptive Learning; Rational Expectations; Bayesian Econometrics; MCMC;

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  1. Bullard, James & Mitra, Kaushik, 2002. "Learning about monetary policy rules," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1105-1129, September.
  2. 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.
  3. Fabio Milani, 2005. "Expectations, Learning and Macroeconomic Persistence," Macroeconomics 0510022, EconWPA.
  4. 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.
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