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Regime Switches in GDP Growth and Volatility: Some International Evidence and Implications for Modelling Business Cycles

Author

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  • Penelope A. Smith

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

  • Peter M. Summers

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

Abstract

This paper has three main objectives. First, we re-examine some recent findings that suggest a structural decline in the variance of GDP growth in the United States. We estimate a univariate model in which both the mean growth rate of GDP and its variance are influenced by latent state variables that follow independent Markov chain processes. We are particularly interested in evidence of increased stability in the U.S. economy, either because of reduced volatility or a narrower gap between growth rates in expansions and recessions. Second, we investigate whether a similar phenomenon has occured in other countries. Finally, we explore the extent to which this more general model is better able to describe the shape of actual business cycles. We find evidence of a reduction in GDP volatility in U.S. data, beginning in late 1984. However, it is less clear that this change represents a structural break. The recent U.S. recession has reduced the probability of being in the low-variance state. Using data from Australia, Canada, Germany, Japan and the United Kingdom, we find evidence of a similar reduction in volatility of GDP growth. The shift for Japan apparently happened in about 1974, and the past decade's poor economic performance seems to have brought a return to the high-variance state. Apart from Germany, the variance reductions in the other countries all occurred within a ten year period between the early 1980's and the early 1990's. Finally, when we test for non-linear effects using Bayes factors, we find that allowing for a switching variance is much more important than a switching mean. Although the hypothesis of homoscedasticity is overwhelmingly rejected, there is little evidence that this model is better able to capture the shape of actual business cycles.

Suggested Citation

  • Penelope A. Smith & Peter M. Summers, 2002. "Regime Switches in GDP Growth and Volatility: Some International Evidence and Implications for Modelling Business Cycles," Melbourne Institute Working Paper Series wp2002n21, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
  • Handle: RePEc:iae:iaewps:wp2002n21
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    References listed on IDEAS

    as
    1. Hansen, Bruce E, 1992. "The Likelihood Ratio Test under Nonstandard Conditions: Testing the Markov Switching Model of GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 61-82, Suppl. De.
    2. Don Harding & Adrian Pagan, 1999. "Dissecting the Cycle," Melbourne Institute Working Paper Series wp1999n13, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    3. Stephen G. Cecchetti & Alfonso Flores-Lagunes & Stefan Krause, 2006. "Has Monetary Policy become more Efficient? a Cross-Country Analysis," Economic Journal, Royal Economic Society, vol. 116(511), pages 408-433, April.
    4. John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
    5. Olivier Blanchard & John Simon, 2001. "The Long and Large Decline in U.S. Output Volatility," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, pages 135-174.
    6. Christopher A. Sims & Tao Zha, 2002. "Macroeconomic switching," Proceedings, Federal Reserve Bank of San Francisco.
    7. 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.
    8. Hess, Gregory D & Iwata, Shigeru, 1997. "Measuring and Comparing Business-Cycle Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 432-444, October.
    9. Koop, Gary & Potter, Simon M., 1998. "Bayes factors and nonlinearity: Evidence from economic time series1," Journal of Econometrics, Elsevier, vol. 88(2), pages 251-281, November.
    10. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, January.
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    Citations

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    Cited by:

    1. Stephen G Cecchetti & Alfonso Flores-Lagunes & Stefan Krause, 2005. "Assessing the Sources of Changes in the Volatility of Real Growth," RBA Annual Conference Volume,in: Christopher Kent & David Norman (ed.), The Changing Nature of the Business Cycle Reserve Bank of Australia.
    2. Mayer, Eric & Scharler, Johann, 2011. "Noisy information, interest rate shocks and the Great Moderation," Journal of Macroeconomics, Elsevier, pages 568-581.
    3. Peter M. Summers, 2005. "What caused the Great Moderation? : some cross-country evidence," Economic Review, Federal Reserve Bank of Kansas City, issue Q III, pages 5-32.
    4. Altug, Sumru G. & Bildirici, Melike, 2010. "Business Cycles around the Globe: A Regime-switching Approach," CEPR Discussion Papers 7968, C.E.P.R. Discussion Papers.
    5. David Shepherd & Robert Dixon, 2008. "The Cyclical Dynamics and Volatility of Australian Output and Employment," The Economic Record, The Economic Society of Australia, pages 34-49.
    6. William Martin & Robert Rowthorn, 2004. "Will Stability Last?," CESifo Working Paper Series 1324, CESifo Group Munich.
    7. de Mello, Luiz & Moccero, Diego, 2011. "Monetary policy and macroeconomic stability in Latin America: The cases of Brazil, Chile, Colombia and Mexico," Journal of International Money and Finance, Elsevier, pages 229-245.
    8. Mayer, Eric & Scharler, Johann, 2011. "Noisy information, interest rate shocks and the Great Moderation," Journal of Macroeconomics, Elsevier, pages 568-581.
    9. Balázs Égert & Rebeca Jiménez-Rodríguez & Evžen Kočenda & Amalia Morales-Zumaquero, 2006. "Structural changes in Central and Eastern European economies: breaking news or breaking the ice?," Economic Change and Restructuring, Springer, pages 85-103.
    10. Penelope A. Smith & Peter M. Summers, 2004. "How Well Do Markov Switching Models Describe Actual Business Cycles? The Case of Synchronization," Melbourne Institute Working Paper Series wp2004n09, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    11. Lin, Justin Yifu & Fardoust, Shahrokh & Rosenblatt, David, 2012. "Reform of the international monetary system : a jagged history and uncertain prospects," Policy Research Working Paper Series 6070, The World Bank.
    12. Mike Shields & Mark Wooden, 2003. "Investigating the Role of Neighbourhood Characteristics in Determining Life Satisfaction," Melbourne Institute Working Paper Series wp2003n24, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    13. Peter M. Summers & Penelope A. Smith, 2005. "How well do Markov switching models describe actual business cycles? The case of synchronization," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 253-274.
    14. Wenjuan Chen, 2011. "On the Continuation of the Great Moderation:New evidence from G7 Countries," SFB 649 Discussion Papers SFB649DP2011-060, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    15. Penelope A. Smith & Peter M. Summers, 2004. "Identification and normalization in Markov switching models of "business cycles"," Research Working Paper RWP 04-09, Federal Reserve Bank of Kansas City.

    More about this item

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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