Bayesian counterfactual analysis of the sources of the great moderation

Chang-Jin Kim, James Morley, Jeremy Piger

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11 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)173-191
Number of pages19
JournalJournal of Applied Econometrics
Volume23
Issue number2
DOIs
Publication statusPublished - 2008 Mar 1

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ASJC Scopus subject areas

  • Economics and Econometrics
  • Social Sciences (miscellaneous)

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