Classical And Modern Business Cycle Measurement: The European Case
Abstracthis 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 time series model proposed in Hamilton (1989a). 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 a statistical instrument to improve the understanding of the business cycle.
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Bibliographic InfoPaper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 9960.
Length: 17 pages
Date of creation: 2001
Date of revision:
BUSINESS CYCLES ; TIME SERIES ; STATISTICAL INSTRUMENTS;
Other versions of this item:
- Hans-Martin Krolzig & Juan Toro, 2004. "Classical and modern business cycle measurement: The European case," Spanish Economic Review, Springer, vol. 7(1), pages 1-21, January.
- 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.
- 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
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