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Learning and the Great Moderation

  • Bullard, James
  • Singh, Aarti

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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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
Contact details of provider: Postal: Sydney, NSW 2006
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Web page: http://sydney.edu.au/arts/economics
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  1. Lee Ohanian & Andres Arias & Gary Hansen, 2005. "Why have business cycle fluctuations become less volatile?," 2005 Meeting Papers 927, Society for Economic Dynamics.
  2. David Andolfatto & Paul Gomme, 1997. "Monetary policy regimes and beliefs," Discussion Paper / Institute for Empirical Macroeconomics 118, Federal Reserve Bank of Minneapolis.
  3. 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.
  4. Alejandro Justiniano & Giorgio E. Primiceri, 2006. "The Time Varying Volatility of Macroeconomic Fluctuations," NBER Working Papers 12022, National Bureau of Economic Research, Inc.
  5. Richard Clarida & Jordi Galí & Mark Gertler, 1997. "Monetary policy rules and macroeconomic stability: Evidence and some theory," Economics Working Papers 350, Department of Economics and Business, Universitat Pompeu Fabra, revised May 1999.
  6. Chang-Jin Kim & Charles Nelson & Jeremy Piger, 2001. "The less volatile U.S. economy: a Bayesian investigation of timing, breadth, and potential explanations," International Finance Discussion Papers 707, Board of Governors of the Federal Reserve System (U.S.).
  7. S. B. Aruoba & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez, 2005. "Comparing Solution Methods for Dynamic Equilibrium Economies," Levine's Bibliography 122247000000000855, UCLA Department of Economics.
  8. Margaret M. McConnell & Gabriel Perez Quiros, 1998. "Output fluctuations in the United States: what has changed since the early 1980s?," Staff Reports 41, Federal Reserve Bank of New York.
  9. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
  10. Michael T. Owyang & Jeremy Piger & Howard J. Wall & Federal Reserve Bank of St. Louis, 2006. "A State-Level Analysis of the Great Moderation," Computing in Economics and Finance 2006 131, Society for Computational Economics.
  11. Shaghil Ahmed & Andrew Levin & Beth Anne Wilson, 2004. "Recent U.S. Macroeconomic Stability: Good Policies, Good Practices, or Good Luck?," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 824-832, August.
  12. Fabio Milani, 2007. "Learning and Time-Varying Macroeconomic Volatility," Working Papers 070802, University of California-Irvine, Department of Economics.
  13. 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.
  14. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, June.
  15. 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.
  16. 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.
  17. James B. Bullard & John Duffy, 2004. "Learning and structural change in macroeconomic data," Working Papers 2004-016, Federal Reserve Bank of St. Louis.
  18. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
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