Statistical and Econometric Analysis of Selected Effects of COVID-19 Pandemic

• Autorzy: Kempa Wojciech , Rydarowska-Kurzbauer Joanna , Halama Marzena , Smuda Elżbieta , Biel Maciej

Identyfikatory

DOI

10.2478/mape-2021-0036

• Języki publikacji: EN

Abstrakty

EN

The paper examines the impact of the COVID-19 pandemic on macroeconomic activity in the selected European countries. The studies are based on monthly and quarterly indicators of GDP, unemployment rates and key indicators of the tourism sector. To present how COVID-19 has affected these macroeconomic variables, statistic data from the three periods are compared. Namely, data are collected from the pre-pandemic period, i.e. the fourth quarter of 2019 as the reference period, the second period covers the first quarter of 2020 and means the beginning of the pandemic, and the third one covers second quarter of 2020, during which the pandemic has spread to all the analyzed countries. The following statistical techniques are used in the research: regression analysis, the hierarchical grouping of agglomerations, k-means method, and selected non-parametric tests (Kruskal-Wallis test for a selected group of countries and Kolmogorov-Smirnov test for a selected pair of countries). The results show the significant impact of the pandemic on the level of gross domestic product, unemployment rate and turism sector. In most cases, a correlation between incidence of COVID-19 infections, unemployment rate and GDP is observed. The statistical techniques also allow to demonstrate the similarities and differences in the response of the economies to the COVID-19 pandemic. Central Statistical Offices of the selected countries are the main data source and for all calculations Statistica version 13.3. is used.

Słowa kluczowe

EN

cluster analysis; COVID-19 pandemic; GDP; regression; unemployment

• Wydawca: STE GROUP ; De Gruyter
• Czasopismo: Multidisciplinary Aspects of Production Engineering
• Rocznik: 2021
• Tom: Vol. 4, Iss. 1
• Strony: 395--407
• Opis fizyczny: Bibliogr. 9 poz., fig., tab.

Twórcy

autor

Kempa Wojciech

• Silesian University of Technology, Poland

• https://orcid.org/0000-0001-9476-2070

autor

Rydarowska-Kurzbauer Joanna

• Silesian University of Technology, Poland

• https://orcid.org/0000-0001-7024-9088

autor

Halama Marzena

• Silesian University of Technology, Poland

autor

Smuda Elżbieta

• Silesian University of Technology, Poland

autor

Biel Maciej

• Silesian University of Technology, Poland

Bibliografia

• 1. F.J. Buera, R.N. Fattal-Jaef, H. Hopenhayn, P.A. Neumeyer, Y. Shin (2021), The economic ripple effects of COVID, NBER Working Paper No 28704.
• 2. F.M. Dekking, C. Kraaikamp, H.P. Lopuhaä, L.E. Meester (2005). A Modern Introduction to Probability and Statistics. London: Springer-Verlag.
• 3. M.H. Dunham (2003). Data Mining. Introductory and Advanced Topics. London: Prentice Hall/Pearson Education.
• 4. D.C. Montgomery, G.C. Runger (2003). Applied Statistics and Probability for Engineers. Third Edition. New York: John Wiley & Sons, Inc.
• 5. K.P. Murphy (2012). Machine Learning. A Probabilistic Perspective. Cambridge: The MIT Press.
• 6. R. Peck, Ch. Olsen, J. Devore (2008). Introduction to Statistics & Data Analysis. Third Edition. Belmont: Thomson Higher Education.
• 7. N. Taleb (2008), The Black Swan. The impact of Highly Improbable, Penguine Books, New York (revised edition).
• 8. World Economic Forum Global Risks Survey 2019–2020 https:// reports.weforum.org/global-risks-report-2020/chapter-one-risks-landscape/ [Accessed 10 Apr. 2020].
• 9. F.J. Buera, R. N. Fattal-Jaef, H. Hopenhayn, P.A. Neumeyer, Y. Shin (2021), The economic ripple effects of COVID, NBER Working Paper No 28704.

Uwagi

Bibliografia zgodna z oryginałem - powtórzony opis bibliograficzny (poz. 1 i 9).

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).