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Business cycle measurement in the presence of structural change: international evidence

  • Krolzig, Hans-Martin

By generalizing Hamiltons model of the US business cycle to a three-regime Markov-switching vector autoregressive model, this paper analyzes regime shifts in the stochastic process of economic growth in the US, Japan and Europe over the last four decades. Empirical evidence is established for the presence of a structural break in the expansionary GDP growth for the US and Japan based on an output-employment MS vector equilibrium correction model, and a structural break in the context of a common European business cycle. For the United States the long expansions of recent years signify basic changes in the business cycles pattern. In the case of Japan we identify long episodes of rapid economic expansions (existing until the mid 1970s) and long economic recessions (as in the 1990s). In Europe we find after an episode of catching-up in the 1970s, convergence in the business cycle pattern which suggests the notion of a European business cycle. The multi-regime Markov-switching VARs proposed are profoundly checked for their economic content and statistical congruency, and are found to provide a sound statistical framework for a comprehensive analysis of the business cycle.

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Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 17 (2001)
Issue (Month): 3 ()
Pages: 349-368

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Handle: RePEc:eee:intfor:v:17:y:2001:i:3:p:349-368
Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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  1. Krolzig, Hans-Martin & Sensier, Marianne, 2000. "A Disaggregated Markov-Switching Model of the Business Cycle in UK Manufacturing," Manchester School, University of Manchester, vol. 68(4), pages 442-60, Special I.
  2. Hamilton, James D & Gang, Lin, 1996. "Stock Market Volatility and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 573-93, Sept.-Oct.
  3. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February.
  4. Hans-Martin Krolzig, 1999. "Statistical Analysis of Cointegrated VAR Processes with Markovian Regime Shifts," Computing in Economics and Finance 1999 1113, Society for Computational Economics.
  5. Phillips, Kerk L., 1991. "A two-country model of stochastic output with changes in regime," Journal of International Economics, Elsevier, vol. 31(1-2), pages 121-142, August.
  6. Mike Artis & Hans-Martin Krolzig & Juan Toro, 2004. "The European business cycle," Oxford Economic Papers, Oxford University Press, vol. 56(1), pages 1-44, January.
  7. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
  8. Andrew J. Filardo, 1994. "International co-movements of business cycles," Research Working Paper 94-11, Federal Reserve Bank of Kansas City.
  9. Garcia, Rene, 1998. "Asymptotic Null Distribution of the Likelihood Ratio Test in Markov Switching Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(3), pages 763-88, August.
  10. Bruce E. Hansen, 1995. "Erratum: The Likelihood ratio Test Under Nonstandard Conditions: Testing the Markov Switching Model of GNP," Boston College Working Papers in Economics 296., Boston College Department of Economics.
  11. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
  12. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
  13. 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 357-84, March.
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