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Learning and the Great Moderation

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  • Bullard, James
  • Singh, Aarti

Abstract

We study a stylized theory of the volatility reduction in the U.S. after 1984—the Great Moderation—which attributes part of the stabilization to less volatile shocks and another part to more difficult inference on the part of Bayesian households attempting to learn the latent state of the economy. We use a standard equilibrium business cycle model with technology following an unobserved regime-switching process. After 1984, according to Kim and Nelson (1999a), the variance of U.S. macroeconomic aggregates declined because boom and recession regimes moved closer together, keeping conditional variance unchanged. In our model this makes the signal extraction problem more difficult for Bayesian households, and in response they moderate their behavior, reinforcing the effect of the less volatile stochastic technology and contributing an extra measure of moderation to the economy. We construct example economies in which this learning effect accounts for about 30 percent of a volatility reduction of the magnitude observed in the postwar U.S. data.

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File URL: http://hdl.handle.net/2123/7092
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Bibliographic Info

Paper provided by University of Sydney, School of Economics in its series Working Papers with number 2009-01.

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Date of creation: Feb 2009
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Handle: RePEc:syd:wpaper:2123/7092

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Keywords: business cycles; regime-switching; Bayesian learning; information;

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References

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  1. Clarida, Richard & Galí, Jordi & Gertler, Mark, 1998. "Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory," CEPR Discussion Papers 1908, C.E.P.R. Discussion Papers.
  2. S. Boragan Aruoba & Jesus Fernandez-Villaverde & Juan Francisco Rubio-Ramirez, 2003. "Comparing solution methods for dynamic equilibrium economies," Working Paper 2003-27, Federal Reserve Bank of Atlanta.
  3. Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez, 2006. "Estimating Macroeconomic Models: A Likelihood Approach," NBER Technical Working Papers 0321, National Bureau of Economic Research, Inc.
  4. Michael T. Owyang & Jeremy M. Piger & Howard J. Wall, 2007. "A state-level analysis of the Great Moderation," Working Papers 2007-003, Federal Reserve Bank of St. Louis.
  5. David Andolfatto & Paul Gomme, 1997. "Monetary Policy Regimes and Beliefs," Working Papers 97002, University of Waterloo, Department of Economics, revised Jan 1997.
  6. Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez, 2007. "Estimating Macroeconomic Models: A Likelihood Approach," Review of Economic Studies, Wiley Blackwell, vol. 74(4), pages 1059-1087, October.
  7. Marco Cagetti & Lars Peter Hansen & Thomas Sargent & Noah Williams, 2002. "Robustness and Pricing with Uncertain Growth," Review of Financial Studies, Society for Financial Studies, vol. 15(2), pages 363-404, March.
  8. Laura Veldkamp, 2003. "Learning Asymmetries in Real Business Cycles," Working Papers 03-21, New York University, Leonard N. Stern School of Business, Department of Economics.
  9. Chang-Jin Kim & Charles Nelson & Jeremy M. Piger, 2003. "The less volatile U.S. economy: a Bayesian investigation of timing, breadth, and potential explanations," Working Papers 2001-016, Federal Reserve Bank of St. Louis.
  10. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  11. Fabio Milani, 2007. "Learning and Time-Varying Macroeconomic Volatility," Working Papers 070802, University of California-Irvine, Department of Economics.
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Cited by:
  1. James B. Bullard, 2009. "Three funerals and a wedding," Review, Federal Reserve Bank of St. Louis, issue Jan, pages 1-12.
  2. Richard Harrison & George Kapetanios & Alasdair Scott & Jana Eklund, 2008. "Breaks in DSGE models," 2008 Meeting Papers 657, Society for Economic Dynamics.
  3. Murray, James, 2011. "Learning and judgment shocks in U.S. business cycles," MPRA Paper 29257, University Library of Munich, Germany.

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