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Bayesian counterfactual analysis of the sources of the great moderation

Author

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  • Chang-Jin Kim

    (Deparment of Economics, Korea University, Seoul, Korea; Department of Economics, University of Washington, Seattle, Washington, USA)

  • James Morley

    (Department of Economics, Washington University, St Louis, Missouri, USA)

  • Jeremy Piger

    (Department of Economics, University of Oregon, Eugene, Oregon, USA)

Abstract

We use counterfactual experiments to investigate the sources of the large volatility reduction in US real GDP growth in the 1980s. Contrary to an existing literature that conducts counterfactual experiments based on classical estimation and point estimates, we consider Bayesian analysis that provides a straightforward measure of estimation uncertainty for the counterfactual quantity of interest. Using Blanchard and Quah's (1989) structural VAR model of output growth and the unemployment rate, we find strong statistical support for the idea that a counterfactual change in the size of structural shocks alone, with no corresponding change in the propagation of these shocks, would have produced the same overall volatility reduction as what actually occurred. Looking deeper, we find evidence that a counterfactual change in the size of aggregate supply shocks alone would have generated a larger volatility reduction than a counterfactual change in the size of aggregate demand shocks alone. We show that these results are consistent with a standard monetary VAR, for which counterfactual analysis also suggests the importance of shocks in generating the volatility reduction, but with the counterfactual change in monetary shocks alone generating a small reduction in volatility. Copyright © 2007 John Wiley & Sons, Ltd.

Suggested Citation

  • Chang-Jin Kim & James Morley & Jeremy Piger, 2008. "Bayesian counterfactual analysis of the sources of the great moderation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(2), pages 173-191.
  • Handle: RePEc:jae:japmet:v:23:y:2008:i:2:p:173-191
    DOI: 10.1002/jae.978
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    References listed on IDEAS

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    1. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-673, September.
    2. Marianne Sensier & Dick van Dijk, 2004. "Testing for Volatility Changes in U.S. Macroeconomic Time Series," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 833-839, August.
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    5. Olivier Blanchard & John Simon, 2001. "The Long and Large Decline in U.S. Output Volatility," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 32(1), pages 135-174.
    6. Robert J Gordon, 2005. "What Caused the Decline in US Business Cycle Volatility?," RBA Annual Conference Volume,in: Christopher Kent & David Norman (ed.), The Changing Nature of the Business Cycle Reserve Bank of Australia.
    7. 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.
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    9. Rudebusch, Glenn D, 2005. "Assessing the Lucas Critique in Monetary Policy Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(2), pages 245-272, April.
    10. Herrera, Ana Maria & Pesavento, Elena, 2005. "The Decline in U.S. Output Volatility: Structural Changes and Inventory Investment," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 462-472, October.
    11. Faust, Jon & Leeper, Eric M, 1997. "When Do Long-Run Identifying Restrictions Give Reliable Results?," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 345-353, July.
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    Citations

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    Cited by:

    1. Craighead, William D. & Tien, Pao-Lin, 2015. "Nominal shocks and real exchange rates: Evidence from two centuries," Journal of International Money and Finance, Elsevier, vol. 56(C), pages 135-157.
    2. Morley, James & Singh, Aarti, 2009. "Inventory Mistakes and the Great Moderation," Working Papers 2009-04, University of Sydney, School of Economics, revised Feb 2015.
    3. Eo, Yunjong & Morley, James C., 2008. "Likelihood-Based Confidence Sets for the Timing of Structural Breaks," MPRA Paper 10372, University Library of Munich, Germany.
    4. James Morley & Aarti Singh, 2016. "Inventory Shocks and the Great Moderation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 699-728, 06.
    5. Charles, Amélie & Darné, Olivier & Kim, Jae H., 2012. "Exchange-rate return predictability and the adaptive markets hypothesis: Evidence from major foreign exchange rates," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1607-1626.
    6. Marcel Förster, 2013. "The Great Moderation: Inventories, Shocks or Monetary Policy?," MAGKS Papers on Economics 201348, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    7. Norhana Endut & James Morley & Pao-Lin Tien, 2015. "The Changing Transmission Mechanism of U.S. Monetary Policy," Discussion Papers 2015-03, School of Economics, The University of New South Wales.
    8. Yunjong Eo & James Morley, 2015. "Likelihood‐ratio‐based confidence sets for the timing of structural breaks," Quantitative Economics, Econometric Society, vol. 6(2), pages 463-497, July.
    9. Enders, Walter & Ma, Jun, 2011. "Sources of the great moderation: A time-series analysis of GDP subsectors," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 67-79, January.

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