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A Bayesian Approach to Counterfactual Analysis of Structural Change

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  • 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)

Abstract

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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    Cited by:

    1. is not listed on IDEAS
    2. Fuentes-Albero, Cristina, 2007. "Technology Shocks, Statistical Models, and The Great Moderation," MPRA Paper 3589, University Library of Munich, Germany.
    3. James Morley & Jeremy Piger, 2006. "The Importance of Nonlinearity in Reproducing Business Cycle Features," Contributions to Economic Analysis, in: Nonlinear Time Series Analysis of Business Cycles, pages 75-95, Emerald Group Publishing Limited.

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