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Published in: Quality & Quantity 6/2014

01-11-2014

Clustering eurozone cycles

Author: Chee-Heong Quah

Published in: Quality & Quantity | Issue 6/2014

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Abstract

Firstly, the study draws out symmetrical subsets of 27 member countries of the European Union (EU27) countries according to their extents of business cycle symmetry with the whole of EA12, quantified by correlations of cycles between the countries’ GDP components and the EA12s GDP. The objective is to evaluate the obtained country groupings against the EA12 countries, the initial members of the euro club. This exercise is implemented using hierarchical cluster analysis for pre- and post-euro periods. As a robustness check, a principal component analysis is applied. Secondly, the analysis deploys a discriminant technique on the cluster analysis solutions to identify the GDP component whose cycle synchronicity contributes most to separation of the countries. In a nutshell, the findings suggest a significant rise in fragmentation within EU27 and within EA12 after euroization and that cycle synchronization involving the GDP component of private investment as a more important determinant to country classifications.

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Footnotes
2
The author of this paper would like to express his greatest gratitude to Afonso and Sequeira (2010) whose findings are used as the input dataset for the present work.
 
3
EA12 comprise of Austria, Belgium, Netherlands, Germany, Finland, France, Ireland, Italy, Luxembourg, Portugal, Spain, and Greece. Whilst the rest euroized on 1 January 1999, Greece qualified in 2000 and was admitted on 1 January 2001. The country codes are placed in Table 1.
 
5
In applying the cluster analysis, the data were normalized to mean zero, unit variance. In this sense, the criteria are equally weighted.
 
6
In general, an effective representation of data requires that the number of clusters be neither too small nor too large to obtain qualitatively meaningful results. Also, it is appropriate to select the number of clusters when the CHI peaks, as those shown here.
 
7
It is called a pooled within-groups matrix because it is obtained by averaging the separate covariance matrices for all groups and then computing the correlation matrix from the pooled-covariance matrix.
 
8
The signs of the coefficients are arbitrary. Negative coefficients could just as well be positive if the signs of the positive coefficients were made negative.
 
9
Values close to 1 indicate that most of the observed variability in the discriminant scores is explained by differences between groups.
 
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Metadata
Title
Clustering eurozone cycles
Author
Chee-Heong Quah
Publication date
01-11-2014
Publisher
Springer Netherlands
Published in
Quality & Quantity / Issue 6/2014
Print ISSN: 0033-5177
Electronic ISSN: 1573-7845
DOI
https://doi.org/10.1007/s11135-013-9966-6

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