Wyniki wyszukiwania - Biblioteka Nauki

1

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

100%

Dwivedi D. N. , Anand A.

Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie

|

2021

|

nr 3(993)

9-26

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.

2

The Text Mining of Public Policy Documents in Response to COVID-19: A Comparison of the United Arab Emirates and the Kingdom of Saudi Arabia

100%

Dwivedi D. N. , Anand A.

Zarządzanie Publiczne / Public Governance

|

2021

|

nr 1(55)

8-22

EN

Objective: The objective of the paper is to analyse publicly available government policy documents of the United Arab Emirates (UAE) and the Kingdom of Saudi Arabia (KSA) in order to identify key topics and themes for these two countries in relation to the COVID-19 response. Research Design & Methods: In view of the availability of large volumes of documents as well as advancement in computing system, text mining has emerged as a significant tool to analyse large volumes of unstructured data. For this paper, we have applied latent semantic analysis and Singular Value Decomposition (SVD) for text clustering. Findings: The results of the analysis of terms indicate similarities of key themes around health and pandemic for the UAE and the KSA. However, the results of text clustering indicate that focus of the UAE’ documents in on ‘Digital’-related terms, whereas for the KSA, it is around ‘International Travel’-related terms. Further analysis of topic modelling demonstrates that topics such as ‘Vaccine Trial’, ‘Economic Recovery’, ‘Health Ministry’, and ‘Digital Platforms’ are common across both the UAE and the KSA. Contribution / Value Added: The study contributes to text-mining literature by providing a framework for analyzing public policy documents at the country level. This can help to understand the key themes in policies of the governments and can potentially aid the identification of the success and failure of various policy measures in certain cases by means of comparing the outcomes. Implications / Recommendations: The results of this study clearly showed that text clustering of unstructured data such as policy documents could be very useful for understanding the themes and orientation topics of the policies.

3

An investigation into the role of synoptic conditions on Central African precipitation variability

51%

Zebaze S. , Pathak R. , Komkoua Mbienda A. J. , Pokam Mba W. , Salunke P. , Anand A. , Vondou D. A. , Lenouo A. , Mishra S. K.

Acta Geophysica

|

2022

|

tom Vol. 70, no 2

943--962

EN

The synoptic precipitation variability over Central Africa (CA) in the March-to-May (MAM) and September-to-November (SON) seasons is investigated in this study. The composite analysis is used to highlight the evolution of synoptic precipitation, related convection, and dynamic fields. Composite analysis findings show that synoptic precipitation anomalies increase/ fade with an increase/decrease in convection, with the largest amplitude over 5° S–10° N and 2.5–20° E in MAM and 5° N–7.5° N and 5–15° E in SON. Precipitation anomalies and convection associated with the synoptic variability progress eastward preceded (followed) by eastward (westward) low-level wind. This synoptic sequence is associated with a downward motion and an inflow, indicating the storm’s presence, which contributes significantly to precipitation initiation over CA. The empirical orthogonal function for precipitation during the SON season is lower than that observed during the MAM season in the context of the size and spatial pattern. During these precipitation occurrences, the zonal moisture component contributes the most to the moisture initiation over the region. The findings of this study show that the observed convergence/ divergence position remains unchanged in intensity irrespective of the positive/negative precipitation events. Overall, the synoptic-scale conditions in MAM contribute around 58% of the total variability in precipitation.