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Time-Varying Network Connectedness of G-7 Economic Policy Uncertainties: A Locally Stationary TVP-VAR Approach

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  • Onur Polat

    (Department of Public Finance, Bilecik Seyh Edebali University)

Abstract

This work analyzes the frequency-dependent network structure of Economic Policy Uncertainties (EPU) across G-7 countries between January 1998 and April 2021. We implement an approach that builds dynamic networks relying on a locally stationary Time-Varying Parameter-Vector Autoregressive model using Quasi-Bayesian Local Likelihood methods. We compute short-, medium-, and long-term network connectedness of G-7 EPUs over a period covering several economic/financial turmoils. Furthermore, we structure short-term network topologies for the Global Financial Crisis (GFC) and the COVID-19 pandemic periods. Findings of the study indicate amplified interdependencies between G-7 EPUs around well-known economic/geopolitical incidents, frequency-dependent connectedness networks among them, and stronger interdependencies than the medium-, and long-term linkages. Finally, we find that short-term spillovers are not persistent in the long-term for both turmoil periods.

Suggested Citation

  • Onur Polat, 2021. "Time-Varying Network Connectedness of G-7 Economic Policy Uncertainties: A Locally Stationary TVP-VAR Approach," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 7(2), pages 47-59, December.
  • Handle: RePEc:ana:journl:v:7:y:2021:i:2:p:47-59
    DOI: 10.22440/wjae.7.2.2
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    as
    1. Poledna, Sebastian & Molina-Borboa, José Luis & Martínez-Jaramillo, Serafín & van der Leij, Marco & Thurner, Stefan, 2015. "The multi-layer network nature of systemic risk and its implications for the costs of financial crises," Journal of Financial Stability, Elsevier, vol. 20(C), pages 70-81.
    2. Rodrik, Dani, 1991. "Policy uncertainty and private investment in developing countries," Journal of Development Economics, Elsevier, vol. 36(2), pages 229-242, October.
    3. Lori Beaman & Ariel BenYishay & Jeremy Magruder & Ahmed Mushfiq Mobarak, 2021. "Can Network Theory-Based Targeting Increase Technology Adoption?," American Economic Review, American Economic Association, vol. 111(6), pages 1918-1943, June.
    4. Hassett, Kevin A & Metcalf, Gilbert E, 1999. "Investment with Uncertain Tax Policy: Does Random Tax Policy Discourage Investment?," Economic Journal, Royal Economic Society, vol. 109(457), pages 372-393, July.
    5. 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.
    6. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 1-15, January.
    7. Cimini, Riccardo, 2015. "Eurozone network “Connectedness” after fiscal year 2008," Finance Research Letters, Elsevier, vol. 14(C), pages 160-166.
    8. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhang, Wei, 2019. "Financial systemic risk measurement based on causal network connectedness analysis," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 290-307.
    9. Byrne, Joseph P. & Davis, E. Philip, 2004. "Permanent and temporary inflation uncertainty and investment in the United States," Economics Letters, Elsevier, vol. 85(2), pages 271-277, November.
    10. Geng, Jiang-Bo & Chen, Fu-Rui & Ji, Qiang & Liu, Bing-Yue, 2021. "Network connectedness between natural gas markets, uncertainty and stock markets," Energy Economics, Elsevier, vol. 95(C).
    11. De Pooter, Michiel & Favara, Giovanni & Modugno, Michele & Wu, Jason, 2021. "Monetary policy uncertainty and monetary policy surprises," Journal of International Money and Finance, Elsevier, vol. 112(C).
    12. Andrew B. Abel & Janice C. Eberly, 1996. "Optimal Investment with Costly Reversibility," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 63(4), pages 581-593.
    13. Singh, Vipul Kumar & Nishant, Shreyank & Kumar, Pawan, 2018. "Dynamic and directional network connectedness of crude oil and currencies: Evidence from implied volatility," Energy Economics, Elsevier, vol. 76(C), pages 48-63.
    14. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    15. Mensi, Walid & Al Rababa'a, Abdel Razzaq & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Asymmetric spillover and network connectedness between crude oil, gold, and Chinese sector stock markets," Energy Economics, Elsevier, vol. 98(C).
    16. Ben S. Bernanke, 1983. "Irreversibility, Uncertainty, and Cyclical Investment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 98(1), pages 85-106.
    17. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    18. Kang, Sang Hoon & Lee, Jang Woo, 2019. "The network connectedness of volatility spillovers across global futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    19. Jesús Fernández-Villaverde & Pablo Guerrón-Quintana & Keith Kuester & Juan Rubio-Ramírez, 2015. "Fiscal Volatility Shocks and Economic Activity," American Economic Review, American Economic Association, vol. 105(11), pages 3352-3384, November.
    20. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," JRFM, MDPI, vol. 13(4), pages 1-23, April.
    21. Balcilar, Mehmet & Demirer, Riza & Gupta, Rangan & van Eyden, Reneé, 2017. "The impact of US policy uncertainty on the monetary effectiveness in the Euro area," Journal of Policy Modeling, Elsevier, vol. 39(6), pages 1052-1064.
    22. Kuzubaş, Tolga Umut & Ömercikoğlu, Inci & Saltoğlu, Burak, 2014. "Network centrality measures and systemic risk: An application to the Turkish financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 203-215.
    23. Caloia, Francesco Giuseppe & Cipollini, Andrea & Muzzioli, Silvia, 2019. "How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study," Energy Economics, Elsevier, vol. 84(C).
    24. Balcilar, Mehmet & Gabauer, David & Umar, Zaghum, 2021. "Crude Oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach," Resources Policy, Elsevier, vol. 73(C).
    25. Kevin A. Hassett, 1999. "Tax Policy and Investment," Books, American Enterprise Institute, number 53049, September.
    26. Dror Y. Kenett & Sary Levy-Carciente & Adam Avakian & H. Eugene Stanley & Shlomo Havlin, 2015. "Dynamical Macroprudential Stress Testing Using Network Theory," Working Papers 15-12, Office of Financial Research, US Department of the Treasury.
    27. Mensi, Walid & Boubaker, Ferihane Zaraa & Al-Yahyaee, Khamis Hamed & Kang, Sang Hoon, 2018. "Dynamic volatility spillovers and connectedness between global, regional, and GIPSI stock markets," Finance Research Letters, Elsevier, vol. 25(C), pages 230-238.
    28. Levy-Carciente, Sary & Kenett, Dror Y. & Avakian, Adam & Stanley, H. Eugene & Havlin, Shlomo, 2015. "Dynamical macroprudential stress testing using network theory," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 164-181.
    29. Zhang, Dayong & Broadstock, David C., 2020. "Global financial crisis and rising connectedness in the international commodity markets," International Review of Financial Analysis, Elsevier, vol. 68(C).
    30. Zhang, Dayong, 2017. "Oil shocks and stock markets revisited: Measuring connectedness from a global perspective," Energy Economics, Elsevier, vol. 62(C), pages 323-333.
    31. Mr. Philip Barrett & Miss Sonali Das & Giacomo Magistretti & Evgenia Pugacheva & Mr. Philippe Wingender, 2021. "After-Effects of the COVID-19 Pandemic: Prospects for Medium-Term Economic Damage," IMF Working Papers 2021/203, International Monetary Fund.
    32. De Pooter, Michiel & Favara, Giovanni & Modugno, Michele & Wu, Jason, 2021. "Reprint: Monetary policy uncertainty and monetary policy surprises," Journal of International Money and Finance, Elsevier, vol. 114(C).
    33. Reboredo, Juan C. & Ugolini, Andrea & Aiube, Fernando Antonio Lucena, 2020. "Network connectedness of green bonds and asset classes," Energy Economics, Elsevier, vol. 86(C).
    34. Sang Hoon Kang & Seong-Min Yoon, 2019. "Dynamic connectedness network in economic policy uncertainties," Applied Economics Letters, Taylor & Francis Journals, vol. 26(1), pages 74-78, January.
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    More about this item

    Keywords

    Dynamic networks; TVP-VAR; Pairwise spillovers; Financial connectedness;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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