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2021 | 3(993) | 9-26

Article title

Trade Heterogeneity in the EU: Insights from the Emergence of COVID-19 Using Time Series Clustering

Title variants

PL
Heterogeniczność handlu w UE – ustalenia dotyczące wpływu COVID-19 z wykorzystaniem metody grupowania szeregów czasowych

Languages of publication

EN

Abstracts

EN
Objective: The objective of the paper is to analyse segmentation of EU-27 countries based on quarterly growth rates of exports and imports by using time series clustering. Research Design & Methods: We applied a time series clustering algorithm using TS nodes in SAS Enterprise Miner. To analyse the impact of the pandemic, we considered clusters based on export and import growth rates for two time periods, pre-emergence and post-COVID-19 emergence. Findings: We find that grouping based on export and import growth rates vary for EU-27 countries. Also, clustering results change significantly for post-COVID-19 emergence compared to pre-COVID-19 emergence. Cyprus emerged as an exception based on export growth rates, while Malta came out as an outlier based on the segmentation of its import growth rates. Implications / Recommendations: The impact and severity of COVID-19 has varied across EU countries, which have shown a varied impact in their trade patterns characterised by growth rates of exports and imports. The clustering analysis presented in the paper helps to explain similarities and differences in trade patterns of EU members during the COVID-19 pandemic to effectively implement and harmonise EU specific trade policies to member countries. Contribution: The study contributes to the literature on EU trade by providing an approach to analysing EU-27 segments using time series clustering analysis. It also enhances the growing literature on the impact of the pandemic on international trade by separating clustering analysis for the COVID-19 period and investigating the drivers for the segmentation.
PL
Cel: Celem artykułu jest ocena wyników segmentacji krajów UE-27 opartej na kwartalnych stopach wzrostu eksportu oraz importu, dokonanej z użyciem metody grupowania szeregów czasowych. Metodyka badań: Zastosowano algorytm grupowania szeregów czasowych z wykorzystaniem narzędzia TS Nodes programu SAS Enterprise Miner. Aby ocenić wpływ pandemii COVID-19, wzięto pod uwagę skupienia krajów wyodrębnione na podstawie stóp wzrostu eksportu i importu dla dwóch okresów: przed pandemią COVID-19 oraz w jej trakcie. Wyniki badań: Ustalono, że skupienia krajów UE-27 wyodrębnione na podstawie stóp wzrostu eksportu oraz stóp wzrostu importu różnią się. Ponadto nastąpiła znacząca zmiana wyników grupowania krajów po pojawieniu się COVID-19 w porównaniu z wynikami dla okresu sprzed pandemii. W przypadku grupowania wykorzystującego stopy wzrostu eksportu krajem odstającym okazał się Cypr, a w przypadku segmentacji na podstawie stóp wzrostu importu była nim Malta. Wnioski: Nasilenie i skutki pandemii COVID-19 różniły się w poszczególnych krajach UE, co znalazło odzwierciedlenie w ich zróżnicowanym wpływie na strukturę handlu poszczególnych krajów, ocenianym na podstawie stóp wzrostu eksportu oraz importu. Zaprezentowana w artykule analiza skupień pomaga wyjaśnić podobieństwa i różnice w strukturze handlu krajów członkowskich UE występujące podczas pandemii COVID-19, co może służyć skutecznemu wdrażaniu i harmonizowaniu szczegółowych polityk handlowych UE w krajach członkowskich. Wkład w rozwój dyscypliny: Opracowanie stanowi wkład w badania z zakresu handlu UE dzięki wykorzystaniu do jego analizy metody grupowania szeregów czasowych w odniesieniu do krajów UE-27. Wzbogaca jednocześnie coraz popularniejszy nurt badań poświęconych wpływowi pandemii COVID-19 na handel międzynarodowy przez propozycję określenia ram czasowych dla analizy skupień w postaci okresu zdefiniowanego przez pandemię COVID-19 i zbadanie czynników wpływających na segmentację.

Contributors

  • SAS Institute, Dubai, UAE
  • Credit Risk Team, HSBC, Kraków, Poland

References

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Document Type

Publication order reference

YADDA identifier

bwmeta1.element.desklight-12fdeedd-e302-4058-bda7-4d2f8c8d1ba7
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