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Clustering Eurozone Countries According to Employee Contributions Before and After COVID-19

In: MODELING AND ADVANCED TECHNIQUES IN MODERN ECONOMICS

Author

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  • Hüseyin Ünözkan
  • Nihan Potas
  • Mehmet Yılmaz

Abstract

Many researchers have tried to analyze economic situations with cluster analyses. In this study, we try to analyze the effects of coronavirus disease 2019 (COVID-19) on 29 Eurozone countries by changes of the clusters. The dataset contains species from the European Union formal data group, and they are gross domestic product (GDP) at current prices per hour worked, average annual hours worked per person employed, GDP at 2015 reference levels adjusted for the impact of terms of trade per person employed, real compensation per employee (deflator GDP: total economy) and real unit labor costs (total economy: ratio of compensation per employee to nominal GDP per person employed). We investigate the economic indicators of two different years independently. The cluster analysis for 2019 gives us two clusters for the 29 Eurozone countries. On the other hand, the cluster analysis with the same data group for 2020 gives three clusters. Some countries dissociate positively, while others are affected by COVID-19 negatively. The study shows that COVID-19 affected Eurozone countries in terms of certain European Union employee data group.

Suggested Citation

  • Hüseyin Ünözkan & Nihan Potas & Mehmet Yılmaz, 2022. "Clustering Eurozone Countries According to Employee Contributions Before and After COVID-19," World Scientific Book Chapters, in: Çağdaş Hakan Aladağ & Nihan Potas (ed.), MODELING AND ADVANCED TECHNIQUES IN MODERN ECONOMICS, chapter 11, pages 221-232, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9781800611757_0011
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    Keywords

    Harmonic Regression; Periodograms; Consumer Price Index; Food Inflation; Turkey; Gaussian Distribution; Europe Union; GDP; Panel Data; Spatial Regression; Measurement Errors; Nonlinear Time Series; Chaotic Time Series; Weibull Distribution; Location Parameters; Fiducial Approach; Hypothesis Testing; Green Swan; Financial Stability; Annex II Countries; Financial Time Series; Kernels; Stock Index; Machine Learning; Statistical Learning; Optimization; WSAR Algorithm; Deep Neural Networks; Phyton; Parameter Estimation; COVID-19; Clustering Analyses; Artificial Neural Networks; Performance Criteria; Time Series Forecasting; Statistical Inference;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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