This article analyses the frequency components of European business cycles using real GDP by employing multiresolution decomposition (MRD) with the use of maximal overlap discrete wavelet transforms (MODWT). Static wavelet variance and correlation analysis is performed, and phasing is studied using co-correlation with the eurozone by scale. Lastly dynamic conditional correlation GARCH models are used to obtain dynamic correlation estimates by scale against the EU to evaluate synchronicity of cycles through time. The general …ndings are that eurozone members fall into one of three categories: i) high static and dynamic correlations at all frequency cycles (e.g. France, Belgium, Germany), ii) low static and dynamic correlations, with little sign of convergence occurring (e.g. Greece), and iii) low static correlation but convergent dynamic correlations (e.g. Finland and Ireland)
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Paper provided by EconWPA in its series Macroeconomics with number
0503015.
Find related papers by JEL classification: E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles O52 - Economic Development, Technological Change, and Growth - - Economywide Country Studies - - - Europe
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