{"id":18499,"date":"2023-07-28T13:02:10","date_gmt":"2023-07-28T10:02:10","guid":{"rendered":"https:\/\/modecon.mnau.edu.ua\/?p=18499"},"modified":"2023-07-28T13:02:10","modified_gmt":"2023-07-28T10:02:10","slug":"ukraine-within-the-system-of","status":"publish","type":"post","link":"https:\/\/modecon.mnau.edu.ua\/en\/ukraine-within-the-system-of\/","title":{"rendered":"Zholos T., Mazurenko V. Ukraine within the system of European business cycles: a cluster analysis"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text]<\/p>\n\n\n\n
JEL Classification<\/strong>: C32; E32; F44
\n<\/span><\/td>\n		DOI<\/b>: https:\/\/doi.org\/10.31521\/modecon.V39(2023)-06<\/a><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/vc_column_text][vc_column_text]Zholos<\/strong> T.\u00a0A., <\/strong>PhD student, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine<\/p>\n

ORCID ID:<\/strong> 0000-0003-3839-0991<\/a>
\ne-mail: <\/strong>taras.zholos@gmail.com<\/a><\/p>\n

Mazurenko V.\u00a0I., <\/strong>Doctor of Sciences (Economics), Associate Professor, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine<\/p>\n

ORCID ID:<\/strong> 0000-0002-7167-215X<\/a>
\ne-mail: <\/strong>mvi1210@ukr.net<\/a><\/p>\n

 <\/p>\n

Ukraine within the system of European business cycles: a cluster analysis<\/strong><\/h2>\n

\u00a0\u00a0<\/strong><\/p>\n

Abstract. Introduction<\/strong>. Categorizing data into related clusters is used in many areas of research, including economics and business statistics. Cluster analysis allows quality data segmentation and visualisation of relatedness between different observations, which is particularly useful when there is a large number of heterogeneous observations. In economic literature, many issues of relevance have been effectively addressed by employing cluster analysis. At the same time, many studies have highlighted important deficiencies, such as some level of subjectivity in choosing the most appropriate methods of cluster analysis and a lack of comprehensive commercially available software that implements new methods, which, in turn, necessitates custom programming and makes it difficult to access and replicate the results obtained by other researchers.<\/p>\n

Purpose.<\/strong> The purpose of this study was to investigate the position of Ukraine among the clusters of European business cycles by extracting the cyclical component of the real GDP series of Ukraine and then comparing it to similarly extracted and analyzed data for 34 other European countries using both hierarchical and non-hierarchical clustering algorithms as implemented in the OriginPro software.<\/p>\n

Results.<\/strong> Changes in detrended GDP values of 35 European countries over the period of time from the 2nd quarter of 2006 to the 4th quarter of 2021 (2006Q2-2021Q4) fall into 4 clusters for variables and 5 clusters for observations (countries). Similarities in the common components of detrended GDP series found using Origin Cluster Analysis were such that the nearest neighbors of Ukraine were Lithuania, Finland and Estonia, with the same order of similarity. Lithuania and Finland clustering with Ukraine was also confirmed by K-means cluster analysis. Hierarchical cluster analysis of country-specific components of the detrended GDP series of 35 European countries followed by K-means cluster analysis showed that for most European countries, the time series of their ln(GDP) values fall into two major clusters, which, with few exceptions, represented Western and Eastern European countries.<\/p>\n

Conclusions.<\/strong> It was shown that the common component of detrended GDP series of Ukraine clearly clustered with those of two Baltic and one Scandinavian EU member state \u2013 Lithuania, Finland and Estonia \u2013 in the order indicated. The country-specific component of the detrended GDP series of Ukraine, both qualitatively as revealed by our hierarchical cluster analysis, and quantitatively as revealed by K-means cluster analysis, clustered with the majority of countries comprising Eastern Europe during the entire period of time under investigation. We conclude that these observations are of both theoretical and practical significance in the framework of the Ukraine-EU integration policy.<\/p>\n

Keywords:<\/strong> business cycles; cluster analysis; hierarchical clustering; K-means clustering; external compound shocks; global risks; European integration.<\/p>\n

References:<\/strong><\/p>\n

    \n
  1. The World by Income and Region. Retrieved from https:\/\/datatopics.worldbank.org\/world-development-indicators\/the-world-by-income-and-region.html [in English].<\/li>\n
  2. The World Bank Atlas method \u2013 detailed methodology. Retrieved from https:\/\/datahelpdesk.worldbank.org\/knowledgebase\/articles\/378832-what-is-the-world-bank-atlas-method [in English].<\/li>\n
  3. Jain, A. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters<\/em>, 31(8), 651-666. Retrieved from https:\/\/doi.org\/10.1016\/j.patrec.2009.09.011 [in English].<\/li>\n
  4. Sethi, S. (1971). Comparative cluster analysis for world markets. Journal of Marketing Research<\/em>, 8(3), 348-354. Retrieved from https:\/\/doi.org\/10.2307\/3149574 [in English].<\/li>\n
  5. Szil\u00e1gyi, G. (1991). Cluster analysis in international comparisons. Journal of Official Statistics,<\/em> 7(4), 425-435 [in English].<\/li>\n
  6. Barr, B.M., Waters, N.M. & Fairbairn, K.J. (1980). The application of cluster analysis to entrepreneurial perception of regional economic environments. Environment and Planning A<\/em>, 12, 869-879 [in English].<\/li>\n
  7. Hoen, A. (2002). Identifying linkages with a cluster-based methodology. Economic Systems Research<\/em>, 14(2), 131-146. Retrieved from https:\/\/econpapers.repec.org\/article\/tafecsysr\/v_3a14_3ay_3a2002_3ai_3a2_3ap_3a131-146.htm [in English].<\/li>\n
  8. Grein, A.F, Sethi, S.P. & Tatum, L.G. (2010). A dynamic analysis of country clusters, the role of corruption, and implications for global firms. East-West Journal of Economics and Business<\/em>, XIII(2), 33-60 [in English].<\/li>\n
  9. Balasankar, V., Penumatsa, S.S.V. & Vital, T.P.R. (2021). Empirical statistical analysis and cluster studies on socio-economic status (SES) dataset. IOP Conference Series: Materials Science and Engineering<\/em>, 1085, 012030. DOI: 10.1088\/1757-899X\/1085\/1\/012030 [in English].<\/li>\n
  10. Brauksa, I. (2013). Use of cluster analysis in exploring economic indicator differences among regions: The case of Latvia. Journal of Economics, Business and Management<\/em>, 1(1), 42-45. DOI: 10.7763\/JOEBM.2013.V1.10 [in English].<\/li>\n
  11. Zhang, Z. & Gao, Y. (2015). Emerging market heterogeneity: Insights from cluster and taxonomy analysis. International Monetary Fund (IMF) Working Papers<\/em>. Working Paper No. 2015\/155. Retrieved from https:\/\/www.imf.org\/en\/Publications\/WP\/Issues\/2016\/12\/31\/Emerging-Market-Heterogeneity-Insights-from-Cluster-and-Taxonomy-Analysis-43085 [in English].<\/li>\n
  12. Scutariu, A-L, \u0218u\u015fu, \u0218, Huidumac-Petrescu, C-E & Gogonea, R-M. (2022). A cluster analysis concerning the behavior of enterprises with e-commerce activity in the context of the COVID-19 pandemic. Journal of Theoretical and Applied Electronic Commerce Research<\/em>, 17(1), 47-68. https:\/\/doi.org\/10.3390\/jtaer17010003 [in English].<\/li>\n
  13. Responding to the Pandemic: IMF Concessional Financing Support for Low-income Countries. International Monetary Fund. Retrieved from https:\/\/www.imf.org\/en\/About\/FAQ\/low-income-countries-concessional-financing-support [in English].<\/li>\n
  14. \u0158ezankov\u00e1, H. (2014). Cluster analysis of economic data. Statistika: Statistics and Economy Journal,<\/em> 94(1), 73-86 [in English].<\/li>\n
  15. Ma, H. (2021). The role of clustering algorithm-based big data processing in information economy development. PLoS ONE<\/em>, 16(3) :e0246718. https:\/\/doi.org\/10.1371\/journal.pone.0246718 [in English].<\/li>\n
  16. Wang, W. & Lu, Y. (2021). Application of clustering analysis of panel data in economic and social research based on R software. Academic Journal of Business & Management<\/em>, 3(10), 98-104. https:\/\/doi.org\/10.25236\/AJBM.2021.031018 [in English].<\/li>\n
  17. Data Catalog. Global Economic Monitor. The World Bank<\/em>. Dataset v.3. Retrieved from https:\/\/datacatalog.worldbank.org\/search\/dataset\/0037798\/Global-Economic-Monitor [in English].<\/li>\n
  18. Phillips, P. C. & Shi, Z. (2021). Boosting: why you can use the HP filter. International Economic Review<\/em>, 62, 521-570. Retrieved from: https:\/\/arxiv.org\/pdf\/1905.00175.pdf [in English].<\/li>\n
  19. OriginLab: Algorithms (Hierarchical Cluster Analysis). Retrieved from https:\/\/www.originlab.com\/doc\/Origin-Help\/HCA-Algorithm [in English].<\/li>\n
  20. OriginLab: Cluster Analysis. Retrieved from https:\/\/www.originlab.com\/doc\/Origin-Help\/Cluster-Analysis [in English].<\/li>\n
  21. OriginLab: Data Analysis>Optimal Cluster Number. Retrieved from https:\/\/www.originlab.com\/fileExchange\/details.aspx?fid=609 [in English].<\/li>\n
  22. Xu, D. & Tian, Y.\u00a0 (2015). Comprehensive survey of clustering algorithms. Annals of Data Science<\/em>, 2(2), 165\u2013193. https:\/\/doi.org\/10.1007\/s40745-015-0040-1 [in English].<\/li>\n<\/ol>\n

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    [vc_row][vc_column][vc_column_text] JEL Classification: C32; E32; F44 DOI: https:\/\/doi.org\/10.31521\/modecon.V39(2023)-06 [\/vc_column_text][vc_column_text]Zholos T.\u00a0A., PhD student, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine ORCID ID: 0000-0003-3839-0991 e-mail: taras.zholos@gmail.com Mazurenko V.\u00a0I., Doctor of Sciences (Economics), Associate Professor, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine ORCID ID: 0000-0002-7167-215X e-mail: mvi1210@ukr.net   Ukraine within the system of European business cycles:
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