IDEAS home Printed from https://ideas.repec.org/p/syd/wpaper/2123-8150.html
   My bibliography  Save this paper

Markov-Switching Models with Evolving Regime-Specific Parameters: Are Post-War Booms or Recessions All Alike?

Author

Listed:
  • Eo, Yunjong
  • Kim, Chang-Jin

Abstract

In this paper, we relax the assumption of constant regime-specific mean growth rates in Hamilton's (1989) two-state Markov-switching model of the business cycle. We first present a benchmark model, in which each regime-specific mean growth rate evolves according to a random walk process over different episodes of booms or recessions. We then present a model with vector error correction dynamics for the regime-specific mean growth rates, by deriving and imposing a condition for the existence of a long-run equilibrium growth rate for real output. In the Bayesian Markov Chain Monte Carlo (MCMC) approach developed in this paper, the counterfactual priors, as well as the hierarchical priors for the regime-specific parameters, play critical roles. By applying the proposed approach to postwar U.S. real GDP growth (1947:Q4-2011:Q3), we uncover the evolving nature of the regime-specific mean growth rates of real output in the U.S. business cycle. An additional feature of the postwar U.S. business cycle that we uncover is a steady decline in the long-run equilibrium output growth. The decline started in the 1950s and ended in the 2000s. Our empirical results also provide partial, if not decisive, evidence that the central bank may have been more successful in restoring the economy back to its long-run equilibrium growth path after unusually severe recessions than after unusually good booms.

Suggested Citation

  • Eo, Yunjong & Kim, Chang-Jin, 2012. "Markov-Switching Models with Evolving Regime-Specific Parameters: Are Post-War Booms or Recessions All Alike?," Working Papers 2012-04, University of Sydney, School of Economics.
  • Handle: RePEc:syd:wpaper:2123/8150
    as

    Download full text from publisher

    File URL: http://www.econ-wpseries.com/2012/201204-02.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Don Harding & Adrian Pagan, 2016. "The Econometric Analysis of Recurrent Events in Macroeconomics and Finance," Economics Books, Princeton University Press, edition 1, number 10744.
    2. Albert, James H & Chib, Siddhartha, 1993. "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 1-15, January.
    3. Atsushi Inoue, 2012. "Mean-Plus-Noise Factor Models: An Empirical Exploration," The Japanese Economic Review, Japanese Economic Association, vol. 63(3), pages 289-309, September.
    4. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Forecasting Time Series Subject to Multiple Structural Breaks," Review of Economic Studies, Oxford University Press, vol. 73(4), pages 1057-1084.
    5. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
    6. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
    7. Giordani, Paolo & Kohn, Robert, 2008. "Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 66-77, January.
    8. Olivier J. Blanchard & Mark W. Watson, 1986. "Are Business Cycles All Alike?," NBER Chapters,in: The American Business Cycle: Continuity and Change, pages 123-180 National Bureau of Economic Research, Inc.
    9. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    10. Gary Koop & Simon M. Potter, 2007. "Estimation and Forecasting in Models with Multiple Breaks," Review of Economic Studies, Oxford University Press, vol. 74(3), pages 763-789.
    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. Pablo A. Guerron-Quintana & Ryo Jinnai, "undated". "Liquidity, Trends and the Great Recession," Working Papers e66, Tokyo Center for Economic Research.
    2. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    3. Hernández-Murillo, Rubén & Owyang, Michael T. & Rubio, Margarita, 2017. "Clustered housing cycles," Regional Science and Urban Economics, Elsevier, vol. 66(C), pages 185-197.
    4. Bełej Mirosław & Kulesza Sławomir, 2012. "Modeling the Real Estate Prices in Olsztyn under Instability Conditions," Folia Oeconomica Stetinensia, De Gruyter Open, vol. 11(1), pages 61-72, January.
    5. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-09 Recession," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 44(1 (Spring), pages 81-156.
    6. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-2009 Recession," NBER Working Papers 18094, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    State- Space Model; MCM; Hamilton Model; Markov Switching; Hierarchical Prior; Evolving Regime- Specific Parameters; Counterfactual Prior; Business Cycle; Bayesian Approach;

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:syd:wpaper:2123/8150. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Vanessa Holcombe). General contact details of provider: http://edirc.repec.org/data/deusyau.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.