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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.

Suggested Citation

  • Bullard, James & Singh, Aarti, 2009. "Learning and the Great Moderation," Working Papers 2009-01, University of Sydney, School of Economics.
  • Handle: RePEc:syd:wpaper:2123/7092
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    Citations

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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. Piero Ferri, 2011. "Macroeconomics of Growth Cycles and Financial Instability," Books, Edward Elgar Publishing, number 14260, June.
    3. Richard Harrison & George Kapetanios & Alasdair Scott & Jana Eklund, 2008. "Breaks in DSGE models," 2008 Meeting Papers 657, Society for Economic Dynamics.
    4. repec:zbw:rwirep:0509 is not listed on IDEAS
    5. Mathias Klein & Christopher Krause, 2014. "Income Redistribution, Consumer Credit,and Keeping up with the Riches," Ruhr Economic Papers 0509, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    6. Murray, James, 2011. "Learning and judgment shocks in U.S. business cycles," MPRA Paper 29257, University Library of Munich, Germany.
    7. Suda, J., 2013. "Belief shocks and the macroeconomy," Working papers 434, Banque de France.
    8. Klein, Mathias & Krause, Christopher, 2014. "Income Redistribution, Consumer Credit,and Keeping up with the Riches," Ruhr Economic Papers 509, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    9. Dan Tortorice, 2016. "The Business Cycles Implications of Fluctuating Long Run Expectations," Working Papers 100, Brandeis University, Department of Economics and International Businesss School.
    10. Luigi Bocola & Nils Gornemann, 2013. "Risk, economic growth and the value of U.S. corporations," Working Papers 13-10, Federal Reserve Bank of Philadelphia.
    11. Gilbert Mbara, 2017. "Business Cycle Dating after the Great Moderation: A Consistent Two – Stage Maximum Likelihood Method," Working Papers 2017-13, Faculty of Economic Sciences, University of Warsaw.
    12. Milani, Fabio, 2014. "Learning and time-varying macroeconomic volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 47(C), pages 94-114.
    13. James Bullard & Jacek Suda & Aarti Singh & Costas Azariadis, 2014. "Debt Overhang and Monetary Policy," 2014 Meeting Papers 948, Society for Economic Dynamics.

    More about this item

    Keywords

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

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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