IDEAS home Printed from https://ideas.repec.org/p/fip/fedlwp/2004-014.html
   My bibliography  Save this paper

A Bayesian approach to counterfactual analysis of structural change

Author

Listed:
  • Chang-Jin Kim
  • James Morley
  • Jeremy M. Piger

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 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 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 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.

Suggested Citation

  • Chang-Jin Kim & James Morley & Jeremy M. Piger, 2006. "A Bayesian approach to counterfactual analysis of structural change," Working Papers 2004-014, Federal Reserve Bank of St. Louis.
    • Handle: RePEc:fip:fedlwp:2004-014
    as

    Download full text from publisher

    File URL: http://research.stlouisfed.org/wp/2004/2004-014.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Sims, Christopher A. & Zha, Tao, 2006. "Does Monetary Policy Generate Recessions?," Macroeconomic Dynamics, Cambridge University Press, vol. 10(2), 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, vol. 8(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.
    8. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Fuentes-Albero, Cristina, 2007. "Technology Shocks, Statistical Models, and The Great Moderation," MPRA Paper 3589, University Library of Munich, Germany.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Owyang, Michael T. & Piger, Jeremy & Wall, Howard J., 2008. "A state-level analysis of the Great Moderation," Regional Science and Urban Economics, Elsevier, vol. 38(6), pages 578-589, November.
    2. 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.
    3. Irvine, F. Owen & Schuh, Scott, 2007. "Interest sensitivity and volatility reductions: Cross-section evidence," International Journal of Production Economics, Elsevier, vol. 108(1-2), pages 31-42, July.
    4. Dynan, Karen E. & Elmendorf, Douglas W. & Sichel, Daniel E., 2006. "Can financial innovation help to explain the reduced volatility of economic activity?," Journal of Monetary Economics, Elsevier, vol. 53(1), pages 123-150, January.
    5. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    6. Steven J. Davis & James A. Kahn, 2008. "Interpreting the Great Moderation: Changes in the Volatility of Economic Activity at the Macro and Micro Levels," Journal of Economic Perspectives, American Economic Association, vol. 22(4), pages 155-180, Fall.
    7. Luca Benati, 2003. "Evolving Post-World War II U.K. Economic Performance," Computing in Economics and Finance 2003 171, Society for Computational Economics.
    8. Nicholas Apergis & Stephen M. Miller, 2007. "Total Factor Productivity and Monetary Policy: Evidence from Conditional Volatility," International Finance, Wiley Blackwell, vol. 10(2), pages 131-152, July.
    9. Norhana Endut & James Morley & Pao-Lin Tien, 2018. "The changing transmission mechanism of US monetary policy," Empirical Economics, Springer, vol. 54(3), pages 959-987, May.
    10. Dimitris Korobilis, 2013. "Assessing the Transmission of Monetary Policy Using Time-varying Parameter Dynamic Factor Models-super-," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 157-179, April.
    11. Luca Gambetti & Jordi Galí, 2009. "On the Sources of the Great Moderation," American Economic Journal: Macroeconomics, American Economic Association, vol. 1(1), pages 26-57, January.
    12. Irvine, F. Owen & Schuh, Scott, 2005. "Inventory investment and output volatility," International Journal of Production Economics, Elsevier, vol. 93(1), pages 75-86, January.
    13. F. Owen Irvine, 2004. "Sales persistence and the reductions in GDP volatility," Working Papers 05-5, Federal Reserve Bank of Boston.
    14. Grydaki, Maria & Bezemer, Dirk, 2013. "The role of credit in the Great Moderation: A multivariate GARCH approach," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4615-4626.
    15. Anton Nakov & Andrea Pescatori, 2010. "Oil and the Great Moderation," Economic Journal, Royal Economic Society, vol. 120(543), pages 131-156, March.
    16. Necati Tekatli, 2007. "Understanding Sources of the Change in International Business Cycles," UFAE and IAE Working Papers 731.08, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    17. Mumtaz, Haroon & Zanetti, Francesco, 2012. "Neutral technology shocks and employment dynamics: results based on an RBC identification scheme," Bank of England working papers 453, Bank of England.
    18. Luigi Paciello, 2011. "Does Inflation Adjust Faster to Aggregate Technology Shocks than to Monetary Policy Shocks?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(8), pages 1663-1684, December.
    19. Kevin J. Stiroh, 2009. "Volatility Accounting: A Production Perspective on Increased Economic Stability," Journal of the European Economic Association, MIT Press, vol. 7(4), pages 671-696, June.
    20. Valerie A. Ramey & Daniel J. Vine, 2004. "Tracking the Source of the Decline in GDP Volatility: An Analysis of the Automobile Industry," NBER Working Papers 10384, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    Monetary policy; Econometrics;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedlwp:2004-014. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anna Oates (email available below). General contact details of provider: https://edirc.repec.org/data/frbslus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.