Business cycles in the euro area defined with coincident economic indicators and predicted with leading economic indicators
AbstractClusters of cyclical turning points in the coincident indicators help us identify and date euro area recessions and recoveries in the past several decades. In the USA and some other countries, composite indexes of coincident indicators (CEI) are used to date classical business cycle turning points; also indexes of leading indicators (LEI) are used to help in the difficult task of predicting these turning points. This paper reviews a selection of the available data for monthly and quarterly euro area coincident and leading indicators. From these data, we develop composite indexes using methods analogous to those tested in the US CEI and LEI published by The Conference Board. We compare the resulting business cycle chronology with the existing alternatives and evaluate our selection of leading indicators in the context of how well they predict current economic activity and its major fluctuations for the euro area. Copyright © 2009 John Wiley & Sons, Ltd.
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.
Volume (Year): 29 (2010)
Issue (Month): 1-2 ()
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Other versions of this item:
- Ataman Ozyildirim & Brian Schaitkin & Victor Zarnowitz, 2008. "Business Cycles in the Euro Area Defined with Coincident Economic Indicators and Predicted with Leading Economic Indicators," Economics Program Working Papers 08-04, The Conference Board, Economics Program.
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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