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Evaluating the Hierarchical Contagion of Economic Policy Uncertainty among the Leading Developed and Developing Economies

Author

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  • Serkan Alkan

    (Faculty of Applied Sciences, Department of Banking and Finance, Tarsus University, Mersin 33000, Turkey)

  • Saffet Akdağ

    (Faculty of Applied Sciences, Department of Banking and Finance, Tarsus University, Mersin 33000, Turkey)

  • Andrew Adewale Alola

    (CREDS-Centre for Research on Digitalization and Sustainability, Inland Norway University of Applied Sciences, 2418 Elverun, Norway
    Faculty of Economics, Administrative and Social Sciences, Nisantasi University, Istanbul 69002, Turkey)

Abstract

An array of global events, including the global financial crisis, natural disasters, and the recent coronavirus pandemic, have consistently shown the vulnerability of global systems and humans to externally undesirable contagions. In order to further provide alternative approaches to information valuation, this study utilized the economic policy uncertainty (EPU) of 21 leading developed and developing economies (Australia, Brazil, Canada, Chile, China, Colombia, Denmark, France, Germany, Greece, India, Ireland, Italy, Japan, Korea, Netherlands, Russia, Spain, Sweden, the United Kingdom, and the United States of America) over the period January 1997 to May 2021. The information theory reveals the hierarchy of degrees of randomness in the EPU indices; it shows the information flow among the EPU indices through the mutual information metric and the graphical illustration of the information flows using network theory. Importantly, the Entropy measures indicate higher predictability of the Netherlands and Ireland’s EPU indices, suggesting that they have less randomness than other indices. Contrarily, Greece and the United Kingdom share the lowest predictability of the EPU indices. Moreover, the complex networks analysis shows that the EPU indices is generally shaped by geographic location. In order of significance, the United States of America’s EPU index exhibits the strongest correlation with other countries’ EPU indices and followed by the EPU indices of France, the United Kingdom (UK), and Germany. In general, the result of the investigation communicates relevant policy measures that potentially ameliorate shocks from external contagions.

Suggested Citation

  • Serkan Alkan & Saffet Akdağ & Andrew Adewale Alola, 2023. "Evaluating the Hierarchical Contagion of Economic Policy Uncertainty among the Leading Developed and Developing Economies," Economies, MDPI, vol. 11(8), pages 1-17, July.
  • Handle: RePEc:gam:jecomi:v:11:y:2023:i:8:p:201-:d:1203189
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    References listed on IDEAS

    as
    1. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    2. Vidal-Tomás, David, 2021. "Transitions in the cryptocurrency market during the COVID-19 pandemic: A network analysis," Finance Research Letters, Elsevier, vol. 43(C).
    3. Cui Jinxin & Zou Huiwen, 2020. "Connectedness Among Economic Policy Uncertainties: Evidence from the Time and Frequency Domain Perspectives," Journal of Systems Science and Information, De Gruyter, vol. 8(5), pages 401-433, October.
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