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

Hüseyin Ünözkan, Nihan Potas and Mehmet Yılmaz

Chapter 11 in Modeling and Advanced Techniques in Modern Economics, 2022, pp 221-232 from World Scientific Publishing Co. Pte. Ltd.

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.

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 (search for similar items in EconPapers)
JEL-codes: C1 C4 C5 C6 C63 (search for similar items in EconPapers)
Date: 2022
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