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The European business cycle

Author

Listed:
  • Mike Artis
  • Hans-Martin Krolzig
  • Juan Toro
Abstract
This paper deals with the existence and identification of a common European growth cycle. Univariate Markov switching autoregressions are used for individual countries in order to detect changes in the mean growth rate of industrial production. A Markov switching vector autoregression model is then used to identify a common cycle in Europe. Three important results are obtained: we find a common unobserved component governing European business cycle dynamics, suggesting the existence of a common business cycle; we propose a dating of the business cycle, both for an index of industrial protection and GDP, and both chronologies appear to be consistent; and finally we retrieve an important set of stylized facts and relate these with those reported for the US. Finally two further issues are investigated: first, the contribution of the European business cycle to the individual country cycles; and second, we undertake an impulse response analysis to investigate the response of each individual country to European expansions and recessions. Copyright 2004, Oxford University Press.

Suggested Citation

  • 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.
    • Handle: RePEc:oup:oxecpp:v:56:y:2004:i:1:p:1-44
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • F43 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Economic Growth of Open Economies
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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