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The European Business Cycle

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Author Info
Mike Artis () (European University Institute, Florence & CEPR.)
Hans-Martin Krolzig () (Institute of Economics and Statistics & Nuffield College, Oxford.)
Juan Toro () (Centro de Estudios Andaluces)

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Abstract

This paper deals with the existence and identification of a common European growth cycle. Univariate Markov switching autoregressions (MS-AR) are used for individual countries in order to detect changes in the mean growth rate of industrial production. A Markov switching vector autoregression model (MS-VAR) is then used to identify a common cycle in Europe. Three important results are obtained. First, we find a common unobserved component governing the business cycle dynamics in Europe, suggesting the existence of a common business cycle. Second, we propose a dating of the business cycle in Europe, both for an index of industrial production (IIP) and gross domestic product (GDP); both chronologies appear to be consistent. Third, we retrieve an important set of stylized facts and relate these with those reported for the US economy (see, among others, French (1993), Warnock y Warnock (2000) and Sichel (1994)).

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Paper provided by Centro de Estudios Andaluces in its series Economic Working Papers at Centro de Estudios Andaluces with number E2002/19.

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Length: 50 pages
Date of creation: 2002
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Handle: RePEc:cea:doctra:e2002_19

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Related research
Keywords: International business cycles; European Union; Markov switching; Structural breaks; Time series analysis.;

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Find related papers by 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
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

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