Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-28T03:15:05.276Z Has data issue: false hasContentIssue false
  • English
  • Français

International Business Cycles and Long-Run Growth: An Analysis with Markov-Switching and Cointegration Methods

Published online by Cambridge University Press:  17 August 2016

Juergen Kähler  and
Volker Marnet
Juergen Kähler
Affiliation:
ZEW & Universität Mannheim
Volker Marnet
Affiliation:
ZEW, Mannheim
Get access

Summary

In this article, we concern ourselves with the modelling of macroeconomic time series within the unit-roots framework. We apply two approaches which seem to be well suited to model business cycles and long-run growth phenomena. First, we apply the Markov-switching model which is built around the idea that a variable may be associated with different regimes. We extend this approach to allow for asymmetries in business cycles and find that with this modification the model identifies regimes which cannot be associated with notions of the business cycle. Second, in a cointegration analysis we examine common stochastic trends and international transmission of macroeconomic shocks. The results show that transient shocks do not vanish, but have long persistent effects. Furthermore, we supplement the cointegration approach with an impulse response analysis and find that there exists a transmission of shocks between countries which indicates great international dependencies in economic activity.

Résumé

Résumé

Dans cet article nous nous intéressons à la modélisation de séries temporelles macro-économiques dans le contexte de l'analyse des racines unitaires. Nous adoptons deux approches qui semblent être adaptées à la modélisation des cycles conjoncturels et des phénomènes de long-terme. D'abord, nous appliquons un modèle markovien à changement de régime, construit autour de l'idée qu'une même variable peut être associée a différents régimes. Cette approche est ensuite étendue a l'examen d'asymétries dans les cycles conjoncturels, ce qui permet d'identifier des régimes non-susceptibles d'être associés au cycle conjoncturel. Ensuite, dans une analyse de co-intégration, nous examinons les tendances stochastiques communes et la transmission internationale des chocs macro-économiques. Les résultats montrent que des chocs transitoires ne disparaissent pas et ont, au contraire, des effets longs et durables. De plus, en effectuant une analyse en termes d'impulsions et de réponses, nous trouvons qu'il existe une transmission des chocs entre pays, indiquant un degré élevé d'interdépendance dans l'activité économique des différents pays.

Keywords

131423211131423211
Type
Part III: Disequilibrium and Business Cycle Analysis
Information
Recherches Économiques de Louvain/ Louvain Economic Review , Volume 58 , Issue 3-4: Output and Employment Fluctuations , 1992 , pp. 399 - 417
Copyright
Copyright © Université catholique de Louvain, Institut de recherches économiques et sociales 1992 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

(*)

We thank Casper de Vries and three anonymous referees for helpful comments and suggestions.

References

REFERENCES

Baum, L.E., Petrie, T. Soules, G., and Weiss, N. (1970), A Maximum Technique Occuring in the Statistical Analysis of Probabilistic Functions of Markov Chains, Annals of Mathematical Statistics, vol. 41, pp. 164–171.Google Scholar
Beveridge, S. and Nelson, C.R. (1981), A New Approach to Decomposition of Economic Time Series into Permanent and Transitory Components with Particular Attention to Measurement of the Business Cycle, Journal of Monetary Economics, vol. 7, pp. 151–174.Google Scholar
Burns, A.F. and Mitchell, W.C. (1946), Measuring Business Cycles, New York, NBER.Google Scholar
Blanchard, O.J. (1981), What is Left of the Multiplier Accelerator? American Economic Review-Papers and Proceedings, vol. 71, pp. 150–154.Google Scholar
Campbell, J.Y. and Mankiw, N.G. (1987), Permanent and Transitory Components in Macroeconomic Fluctuations, American Economic Review-Papers and Proceedings, vol. 77, pp. 111–117.Google Scholar
Cochrane, J.H. (1988), How Big is the Random Walk in GNP? Journal of Political Economy, vol. 96, pp. 893–920.Google Scholar
Delong, J.B. and Summers, L.H. (1986), Are Business Cycles Symmetrical? In: Gordon, R.J. (ed.), The American Business Cycle, Chicago, Chicago University Press, pp. 166–179.Google Scholar
Dickey, D.A., Bell, W.R., and Miller, R.B. (1986), Unit Roots in Time Series Models: Tests and Implications, American Statistician, vol. 40, pp. 12–26.Google Scholar
Granger, C.W.J., and Newbold, P. (1974), Spurious Regressions in Econometrics, Journal of Econometrics, vol. 2, pp. 111–120.Google Scholar
Hamilton, J.D. (1989), A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle, Econometrica, vol. 57, pp. 357–384.Google Scholar
Hamilton, J.D. (1991), Estimation, Inference, and Forecasting of Time Series Subject to Changes in Regime, in: Rao, C.R. and Maddala, G.S. (eds.), Handbook of Statistics, forthcoming.Google Scholar
Harvey, A.C. (1985), Trends and Cycles in Macroeconomic Time Series, Journal of Business and Economic Statistics, vol. 3, pp. 216–227.Google Scholar
Johansen, S. (1988), Statistical Analysis of Cointegrated Vectors, Journal of Economic Dynamics and Control, vol. 12, pp. 231–254.Google Scholar
Johansen, S. and Juselius, K. (1990), Maximum Likelihood Estimation and Inference on Cointegration - with Applications to the Demand for Money, Oxford Bulletin of Economics and Statistics, vol. 52, pp. 169–210.Google Scholar
King, R., Plosser, C.I. J.H., Stock and Watson, M.W. (1991), Stochastic Trends and Economic Fluctuations, American Economic Review, vol. 81, pp. 819–840.Google Scholar
Lam, P.-S. (1990), The Hamilton Model with a General Autoregressive Component: Estimation and Comparison with Other Models of Economic Time Series, Journal of Monetary Economics, vol. 26, pp. 409–432.Google Scholar
Lindgren, G. (1978), Markov Regime Models for Mixed Distributions and Switching Regressions, Scandinavian Journal of Statistics, vol. 5, pp. 81–91.Google Scholar
Lo, A.W. and Mackinlay, A.C. (1988), Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test, The Review of Financial Studies, vol. 1, pp. 41–66.Google Scholar
Lo, A.W. and Mackinlay, A.C. (1989), The Size and Power of the Variance Ratio Test in Finite Samples. A Monte Carlo Investigation, Journal of Econometrics, vol. 40, pp. 203–238.Google Scholar
Lutkepohl, H. (1991), Introduction to Multiple Time Series Analysis, Berlin, Springer-Verlag.Google Scholar
Lutkepohl, H. and Reimers, H.-E. (1992), Impulse Response Analysis of Cointegrated Systems, Journal of Economic Dynamics and Control, vol. 16, pp. 53–78.Google Scholar
Moore, G.H. and Moore, M.H. (1985), International Economic Indicators, Westport, Greenwood Press.Google Scholar
Nelson, C.R. and Plosser, C.I. (1982), Trends and Random Walks in Macroeconomic Time Series, Journal of Monetary Economics, vol. 10, pp. 139–162.Google Scholar
OECD (1980), Quarterly National Accounts Bulletin, n° 4.Google Scholar
OECD (1991), Quarterly National Accounts Bulletin, n° 3 .Google Scholar
Phillips, K.L. (1991), A Two-Country Model of Stochastic Output with Changes in Regime, Journal of International Economics, vol. 31, pp. 121–142.Google Scholar
Rose, A.K. (1986), Four Paradoxes in GNP, Economics Letters, vol. 22, pp. 137–141.Google Scholar
Stock, J.H. and Watson, M.W. (1988), Variable Trends in Economic Time Series, Journal of Economic Perspectives, vol. 2, pp. 147–174.Google Scholar
Zarnowitz, V. and Moore, G.H. (1986), Major Changes in Cyclical Behavior, in: Gordon, R.J. (ed.), The American Business Cycle, Ch. 9, Chicago, Chicago University Press.Google Scholar
Zarnowitz, V. (1991), What is a Business Cycle? NBER Working Paper n° 3863.Google Scholar