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Classical and Modern Business Cycle Measurement: The European Case

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Abstract

This paper intends to harmonize two different approaches to the analysis of the business cycle and in doing so it retrieves the stylized facts of the business cycle in Europe. We start with the “classical” approach proposed in Burns and Mitchell (1946) of dating and analyzing the business cycle; we then adopt the “modern” alternative: the Markov-switching vector autoregression (MS-VAR). The model's regime probabilities provide an optimal statistical inference of the turning point of the European business cycle. For assessing the capacity of the parametric approach to generate the stylized facts of the classical cycle in Europe, the stylized facts of the original data are compared to those of simulated data. The MS-VAR model is shown to be a good candidate for use as an statistical instrument to improve the understanding of the business cycle.

Suggested Citation

  • Hans-Martin Krolzig & Juan Toro, 2002. "Classical and Modern Business Cycle Measurement: The European Case," Economic Working Papers at Centro de Estudios Andaluces E2002/20, Centro de Estudios Andaluces.
    • Handle: RePEc:cea:doctra:e2002_20
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    More about this item

    Keywords

    International business cycles; European Union; Markov Switching; Structural breaks; Time series analysis.;
    All these keywords.

    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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