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

  • 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)

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.

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Paper provided by Melbourne Institute of Applied Economic and Social Research, The University of Melbourne in its series Melbourne Institute Working Paper Series with number wp2002n21.

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Length: 38 pages
Date of creation: Nov 2002
Date of revision:
Handle: RePEc:iae:iaewps:wp2002n21
Contact details of provider: Postal: Melbourne Institute of Applied Economic and Social Research, The University of Melbourne, Victoria 3010 Australia
Phone: +61 3 8344 2100
Fax: +61 3 8344 2111
Web page: http://www.melbourneinstitute.com/
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  1. 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.
  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. Hess, Gregory D & Iwata, Shigeru, 1997. "Measuring and Comparing Business-Cycle Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 432-44, October.
  4. John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
  5. Christopher Sims & Tao Zha, 2002. "Macroeconomic switching," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
  6. 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.
  7. 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, vol. 32(1), pages 135-174.
  8. 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 S61-82, Suppl. De.
  9. 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, June.
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