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A Bayesian Approach to Counterfactual Analysis of Structural Change


  • Chang-Jin Kim University of Washington,, ,Jeremy Piger, Federal Reserve Bank of St. Louis

    (Korea University and University of Washington)

  • James Morley

    (Washington University in St. Louis)

  • Jeremy Piger

    (Federal Reserve Bank of St. Louis)


In this paper, we develop a Bayesian approach to counterfactual analysis of structural change. Contrary to previous analysis based on classical point estimates, this approach provides a straightforward measure of estimation uncertainty for the counterfactual quantity of interest. We apply the Bayesian counterfactual analysis to examine the sources of the volatility reduction in U.S. real GDP growth in the 1980s. Using a 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 only, with no corresponding change in propagation, would have produced the same overall volatility reduction that actually occurred. Looking deeper, we find evidence that a counterfactual change in the size of aggregate supply shocks only would have generated a larger volatility reduction than a counterfactual change in the size of aggregate demand shocks only. 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 only generating a small reduction in volatility

Suggested Citation

  • Chang-Jin Kim University of Washington,, ,Jeremy Piger, Federal Reserve Bank of St. Louis & James Morley & Jeremy Piger, 2006. "A Bayesian Approach to Counterfactual Analysis of Structural Change," Computing in Economics and Finance 2006 259, Society for Computational Economics.
  • Handle: RePEc:sce:scecfa:259

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    References listed on IDEAS

    1. Sims, Christopher A. & Zha, Tao, 2006. "Does Monetary Policy Generate Recessions?," Macroeconomic Dynamics, Cambridge University Press, vol. 10(02), pages 231-272, April.
    2. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    3. James H. Stock & Mark W. Watson, 2003. "Has the Business Cycle Changed and Why?," NBER Chapters,in: NBER Macroeconomics Annual 2002, Volume 17, pages 159-230 National Bureau of Economic Research, Inc.
    4. Kim, Chang-Jin & Nelson, Charles R & Piger, Jeremy, 2004. "The Less-Volatile U.S. Economy: A Bayesian Investigation of Timing, Breadth, and Potential Explanations," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 80-93, January.
    5. James A. Kahn & Margaret M. McConnell & Gabriel Perez-Quiros, 2002. "On the causes of the increased stability of the U.S. economy," Economic Policy Review, Federal Reserve Bank of New York, issue May, pages 183-202.
    6. Jean Boivin & Marc P. Giannoni, 2006. "Has Monetary Policy Become More Effective?," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 445-462, August.
    7. Chang-Jin Kim & Charles R. Nelson, 1999. "Has The U.S. Economy Become More Stable? A Bayesian Approach Based On A Markov-Switching Model Of The Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 608-616, November.
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    Cited by:

    1. Fuentes-Albero, Cristina, 2007. "Technology Shocks, Statistical Models, and The Great Moderation," MPRA Paper 3589, University Library of Munich, Germany.
    2. James Morley & Jeremy M. Piger, 2005. "The importance of nonlinearity in reproducing business cycle features," Working Papers 2004-032, Federal Reserve Bank of St. Louis.

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