Time-Varying Network Connectedness of G-7 Economic Policy Uncertainties: A Locally Stationary TVP-VAR Approach
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Dec 10, 2021
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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.
JEL classification: C10, C40, C58
Keywords: Dynamic networks, TVP-VAR, Pairwise spillovers, Financial connectedness
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Polat, O. (2021) “Time-Varying Network Connectedness of G-7 Economic Policy Uncertainties: A Locally Stationary TVP-VAR Approach”, World Journal of Applied Economics, 7(2), pp. 47-59. doi: 10.22440/wjae.7.2.2.
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Research Articles
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References
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Bernanke, B. S. (1983). Irreversibility, Uncertainty, and Cyclical Investment. The Quarterly Journal of Economics, 98 (1), 85–106. doi:10.2307/1885568
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Byrne, J. P., & Davis, E. P. (2004). Permanent and temporary inflation uncertainty and investment in the United States. Economics Letters, 85 (2), 271–277. doi:10.1016/j.econlet.2004.04.015
Caloia, F. G., Cipollini, A., & Muzzioli, S. (2019). How do normalization schemes affect net spillovers? a replication of the diebold and yilmaz (2012) study. Energy Economics, 84 , 104536. doi:10.1016/j.eneco.2019.104536
Cimini, R. (2015). Eurozone network “Connectedness” after fiscal year 2008. Finance Research Letters, 14 , 160–166. doi:10.1016/j.frl.2015.05.003
Cresswell, K. M., Worth, A., & Sheikh, A. (2010). Actor-Network Theory and its role in understanding the implementation of information technology developments in healthcare. BMC Medical Informatics and Decision Making, 10 (67), 1–11. doi:10.1186/1472-6947-10-67
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Demirer, M., Diebold, F. X., Liu, L., & Yilmaz, K. (2018). Estimating global bank network connectedness. Journal of Applied Econometrics, 33 (1), 1–15. doi:10.1002/jae.2585
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Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions:Measuring the connectedness of financial firms. Journal of Econometrics, 182 (1), 119–134. doi:10.1016/j.jeconom.2014.04.012
Ellington, M., & Barunik, J. (2020). Dynamic Networks in Large Financial and Economic Systems. Available at SSRN 3651134 . doi:10.2139/ssrn.3651134
Fenwick, T. J. (2010). (un)Doing standards in education with actor-network theory. Journal of Education Policy, 25 (2), 117–133. doi:10.1080/02680930903314277
Fernandez-Villaverde, J., Guerron-Quintana, P., Kuester, K., & Rubio-Ramirez, J. (2015). Fiscal Volatility Shocks and Economic Activity. American Economic Review, 105 (11), 3352–3384. doi:10.1257/aer.20121236
Geng, J.-B., Chen, F.-R., Ji, Q., & Liu, B.-Y. (2021). Network connectedness between natural gas markets, uncertainty and stock markets. Energy Economics, 95 , 105001. doi:10.1016/j.eneco.2020.105001
Gong, X.-L., Liu, X.-H., Xiong, X., & Zhang, W. (2019). Financial systemic risk measurement based on causal network connectedness analysis. International Review of Economics & Finance, 64 , 290–307. doi:10.1016/j.iref.2019.07.004
Hassett, K. A., & Metcalf, G. E. (1999). Investment with Uncertain Tax Policy: Does Random Tax Policy Discourage Investment. The Economic Journal, 109 (457), 372–393. doi:10.1111/1468-0297.00453
IMF. (2021). World Economic Outlook. https://www.imf.org/en/Publications/WEO/Issues/2021/03/23/world-economic-outlook-april-2021.
Iyamu, T., & Mgudlwa, S. (2018). Transformation of healthcare big data through the lens of actor network theory. International Journal of Healthcare Management, 11 (3), 182–192. doi:10.1080/20479700.2017.1397340
Kang, S. H., & Lee, J. W. (2019). The network connectedness of volatility spillovers across global futures markets. Physica A: Statistical Mechanics and its Applications, 526 , 120756. doi:10.1016/j.physa.2019.03.121
Kang, S. H., & Yoon, S.-M. (2019). Dynamic connectedness network in economic policy uncertainties. Applied Economics Letters, 26 (1), 74–78. doi:10.1080/13504851.2018.1438580
Khashanah, K., & Alsulaiman, T. (2016). Network theory and behavioral finance in a heterogeneous market environment. Complexity, 21 (S2), 530–554. doi:10.1002/cplx.21834
Kuzubas, T. U., Omercikoglu, I., & Saltoglu, B. (2014). Network centrality measures and systemic risk: An application to the Turkish financial crisis. Physica A: Statistical Mechanics and its Applications, 405 , 203–215. doi:10.1016/j.physa.2014.03.006
Levy-Carciente, S., Kenett, D. Y., Avakian, A., Stanley, H. E., & Havlin, S. (2015). Dynamical macroprudential stress testing using network theory. Journal of Banking & Finance, 59 , 164–181. doi:10.1016/j.jbankfin.2015.05.008
Mensi, W., Al Rababa’a, A. R., Vo, X. V., & Kang, S. H. (2021). Asymmetric spillover and network connectedness between crude oil, gold, and Chinese sector stock markets. Energy Economics, 98 , 105262. doi:10.1016/j.eneco.2021.105262
Mensi, W., Boubaker, F. Z., Al-Yahyaee, K. H., & Kang, S. H. (2018). Dynamic volatility spillovers and connectedness between global, regional, and GIPSI stock markets. Finance Research Letters, 25 , 230–238. doi:10.1016/j.frl.2017.10.032
Murdoch, J. (2001). Ecologising Sociology: Actor-Network Theory, Coconstruction and the Problem of Human Exemptionalism. Sociology, 35 (1), 111–133. doi:10.1017/S0038038501000074
Muutzel, S. (2009). Networks as Culturally Constituted Processes: A Comparison of Relational Sociology and Actor-network Theory. Current Sociology, 57 (6), 871–887. doi:10.1177/0011392109342223
Poledna, S., Molina-Borboa, J. L., Martinez-Jaramillo, S., van der Leij, M., & Thurner, S. (2015). The multi-layer network nature of systemic risk and its implications for the costs of financial crises. Journal of Financial Stability, 20 , 70–81. doi:10.1016/j.jfs.2015.08.001
Reboredo, J. C., Ugolini, A., & Aiube, F. A. L. (2020). Network connectedness of green bonds and asset classes. Energy Economics, 86 , 104629. doi:10.1016/j.eneco.2019.104629
Rodrik, D. (1991). Policy uncertainty and private investment in developing countries.Journal of Development Economics, 36 (2), 229–242. doi:10.1016/0304-3878(91)90034-S
Romer, P. M. (1990). Endogenous Technological Change. Journal of Political Economy, 98 (5, Part 2), S71-S102. doi:10.1086/261725
Shim, Y., & Shin, D.-H. (2016). Analyzing China’s Fintech Industry from the Perspective of Actor–Network Theory. Telecommunications Policy, 40 (2), 168–181. doi:10.1016/j.telpol.2015.11.005
Singh, V. K., Nishant, S., & Kumar, P. (2018). Dynamic and directional network connectedness of crude oil and currencies: Evidence from implied volatility. Energy Economics, 76 , 48–63. doi:10.1016/j.eneco.2018.09.018
Valente, T. W., & Pitts, S. R. (2017). An Appraisal of Social Network Theory and Analysis as Applied to Public Health: Challenges and Opportunities. Annual Review of Public Health, 38 (1), 103–118. doi:10.1146/annurev-publhealth-031816-044528
Weiske, C., Petzold, K., & Schad, H. (2015). Multi-Local Living – The Approaches of Rational Choice Theory, Sociology of Everyday Life and Actor-Network Theory. Tijdschrift voor Economische en Sociale Geografie, 106 (4), 392–408. doi:10.1111/tesg.12157
Zhang, D. (2017). Oil shocks and stock markets revisited: Measuring connectedness from a global perspective. Energy Economics, 62 , 323–333. doi:10.1016/j.eneco.2017.01.009
Zhang, D., & Broadstock, D. C. (2020). Global financial crisis and rising connectedness in the international commodity markets. International Review of Financial Analysis, 68 , 101239. doi:10.1016/j.irfa.2018.08.003
Zhang, Z., & Heydon, R. (2016). The changing landscape of literacy curriculum in a Sino-Canada transnational education programme: an actor-network theory informed case study. Journal of Curriculum Studies, 48 (4), 547–564. doi:10.1080/00220272.2015.1090626
Al-Thaqeb, S. A., Algharabali, B. G., & Alabdulghafour, K. T. (2020). The Pandemic and Economic Policy Uncertainty. International Journal of Finance & Economics, 1-11. doi:10.1002/ijfe.2298
Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions. Journal of Risk and Financial Management, 13 (4). doi:10.3390/jrfm13040084
Bagheri, E., & Ebrahimi, S. B. (2020). Estimating Network Connectedness of Financial Markets and Commodities. Journal of Systems Science and Systems Engineering, 29 , 572–589. doi:10.1007/s11518-020-5465-1
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131 (4), 1593–1636. doi:10.1093/qje/qjw024
Balcilar, M., Demirer, R., Gupta, R., & van Eyden, R. (2017). The impact of US policy uncertainty on the monetary effectiveness in the Euro area. Journal of Policy Modeling, 39 (6), 1052–1064. doi:10.1016/j.jpolmod.2017.09.002
Balcilar, M., Gabauer, D., & Umar, Z. (2021). Crude Oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach. Resources Policy, 73 , 102219. doi:10.1016/j.resourpol.2021.102219
Barrett, P., Das, S., Magistretti, G., Pugacheva, E., & Wingender, P. (2021). After-Effects of the COVID-19 Pandemic: Prospects for Medium-Term Economic Damage (IMF Working Paper No. WP/21/203). International Monetary Fund. https://www.imf.org/en/Publications/WP/Issues/2021/07/30/After-Effects-of-the-COVID-19-Pandemic-Prospects-for-Medium-Term-Economic-Damage-462898.
Beaman, L., BenYishay, A., Magruder, J., & Mobarak, A. M. (2021, June). Can Network Theory-Based Targeting Increase Technology Adoption? American Economic Review, 111 (6), 1918-43. doi:10.1257/aer.20200295
Bernanke, B. S. (1983). Irreversibility, Uncertainty, and Cyclical Investment. The Quarterly Journal of Economics, 98 (1), 85–106. doi:10.2307/1885568
Billio, M., Getmansky, M., Lo, A. W., & Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 104 (3), 535–559. doi:10.1016/j.jfineco.2011.12.010
Byrne, J. P., & Davis, E. P. (2004). Permanent and temporary inflation uncertainty and investment in the United States. Economics Letters, 85 (2), 271–277. doi:10.1016/j.econlet.2004.04.015
Caloia, F. G., Cipollini, A., & Muzzioli, S. (2019). How do normalization schemes affect net spillovers? a replication of the diebold and yilmaz (2012) study. Energy Economics, 84 , 104536. doi:10.1016/j.eneco.2019.104536
Cimini, R. (2015). Eurozone network “Connectedness” after fiscal year 2008. Finance Research Letters, 14 , 160–166. doi:10.1016/j.frl.2015.05.003
Cresswell, K. M., Worth, A., & Sheikh, A. (2010). Actor-Network Theory and its role in understanding the implementation of information technology developments in healthcare. BMC Medical Informatics and Decision Making, 10 (67), 1–11. doi:10.1186/1472-6947-10-67
De Pooter, M., Favara, G., Modugno, M., &Wu, J. (2021). Monetary policy uncertainty and monetary policy surprises. Journal of International Money and Finance, 112 , 102323. doi:10.1016/j.jimonfin.2020.102323
Demirer, M., Diebold, F. X., Liu, L., & Yilmaz, K. (2018). Estimating global bank network connectedness. Journal of Applied Econometrics, 33 (1), 1–15. doi:10.1002/jae.2585
Diebold, F. X., Liu, L., & Yilmaz, K. (2017). Commodity Connectedness (Working Paper No. 23685). National Bureau of Economic Research. doi:10.3386/w23685
Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions:Measuring the connectedness of financial firms. Journal of Econometrics, 182 (1), 119–134. doi:10.1016/j.jeconom.2014.04.012
Ellington, M., & Barunik, J. (2020). Dynamic Networks in Large Financial and Economic Systems. Available at SSRN 3651134 . doi:10.2139/ssrn.3651134
Fenwick, T. J. (2010). (un)Doing standards in education with actor-network theory. Journal of Education Policy, 25 (2), 117–133. doi:10.1080/02680930903314277
Fernandez-Villaverde, J., Guerron-Quintana, P., Kuester, K., & Rubio-Ramirez, J. (2015). Fiscal Volatility Shocks and Economic Activity. American Economic Review, 105 (11), 3352–3384. doi:10.1257/aer.20121236
Geng, J.-B., Chen, F.-R., Ji, Q., & Liu, B.-Y. (2021). Network connectedness between natural gas markets, uncertainty and stock markets. Energy Economics, 95 , 105001. doi:10.1016/j.eneco.2020.105001
Gong, X.-L., Liu, X.-H., Xiong, X., & Zhang, W. (2019). Financial systemic risk measurement based on causal network connectedness analysis. International Review of Economics & Finance, 64 , 290–307. doi:10.1016/j.iref.2019.07.004
Hassett, K. A., & Metcalf, G. E. (1999). Investment with Uncertain Tax Policy: Does Random Tax Policy Discourage Investment. The Economic Journal, 109 (457), 372–393. doi:10.1111/1468-0297.00453
IMF. (2021). World Economic Outlook. https://www.imf.org/en/Publications/WEO/Issues/2021/03/23/world-economic-outlook-april-2021.
Iyamu, T., & Mgudlwa, S. (2018). Transformation of healthcare big data through the lens of actor network theory. International Journal of Healthcare Management, 11 (3), 182–192. doi:10.1080/20479700.2017.1397340
Kang, S. H., & Lee, J. W. (2019). The network connectedness of volatility spillovers across global futures markets. Physica A: Statistical Mechanics and its Applications, 526 , 120756. doi:10.1016/j.physa.2019.03.121
Kang, S. H., & Yoon, S.-M. (2019). Dynamic connectedness network in economic policy uncertainties. Applied Economics Letters, 26 (1), 74–78. doi:10.1080/13504851.2018.1438580
Khashanah, K., & Alsulaiman, T. (2016). Network theory and behavioral finance in a heterogeneous market environment. Complexity, 21 (S2), 530–554. doi:10.1002/cplx.21834
Kuzubas, T. U., Omercikoglu, I., & Saltoglu, B. (2014). Network centrality measures and systemic risk: An application to the Turkish financial crisis. Physica A: Statistical Mechanics and its Applications, 405 , 203–215. doi:10.1016/j.physa.2014.03.006
Levy-Carciente, S., Kenett, D. Y., Avakian, A., Stanley, H. E., & Havlin, S. (2015). Dynamical macroprudential stress testing using network theory. Journal of Banking & Finance, 59 , 164–181. doi:10.1016/j.jbankfin.2015.05.008
Mensi, W., Al Rababa’a, A. R., Vo, X. V., & Kang, S. H. (2021). Asymmetric spillover and network connectedness between crude oil, gold, and Chinese sector stock markets. Energy Economics, 98 , 105262. doi:10.1016/j.eneco.2021.105262
Mensi, W., Boubaker, F. Z., Al-Yahyaee, K. H., & Kang, S. H. (2018). Dynamic volatility spillovers and connectedness between global, regional, and GIPSI stock markets. Finance Research Letters, 25 , 230–238. doi:10.1016/j.frl.2017.10.032
Murdoch, J. (2001). Ecologising Sociology: Actor-Network Theory, Coconstruction and the Problem of Human Exemptionalism. Sociology, 35 (1), 111–133. doi:10.1017/S0038038501000074
Muutzel, S. (2009). Networks as Culturally Constituted Processes: A Comparison of Relational Sociology and Actor-network Theory. Current Sociology, 57 (6), 871–887. doi:10.1177/0011392109342223
Poledna, S., Molina-Borboa, J. L., Martinez-Jaramillo, S., van der Leij, M., & Thurner, S. (2015). The multi-layer network nature of systemic risk and its implications for the costs of financial crises. Journal of Financial Stability, 20 , 70–81. doi:10.1016/j.jfs.2015.08.001
Reboredo, J. C., Ugolini, A., & Aiube, F. A. L. (2020). Network connectedness of green bonds and asset classes. Energy Economics, 86 , 104629. doi:10.1016/j.eneco.2019.104629
Rodrik, D. (1991). Policy uncertainty and private investment in developing countries.Journal of Development Economics, 36 (2), 229–242. doi:10.1016/0304-3878(91)90034-S
Romer, P. M. (1990). Endogenous Technological Change. Journal of Political Economy, 98 (5, Part 2), S71-S102. doi:10.1086/261725
Shim, Y., & Shin, D.-H. (2016). Analyzing China’s Fintech Industry from the Perspective of Actor–Network Theory. Telecommunications Policy, 40 (2), 168–181. doi:10.1016/j.telpol.2015.11.005
Singh, V. K., Nishant, S., & Kumar, P. (2018). Dynamic and directional network connectedness of crude oil and currencies: Evidence from implied volatility. Energy Economics, 76 , 48–63. doi:10.1016/j.eneco.2018.09.018
Valente, T. W., & Pitts, S. R. (2017). An Appraisal of Social Network Theory and Analysis as Applied to Public Health: Challenges and Opportunities. Annual Review of Public Health, 38 (1), 103–118. doi:10.1146/annurev-publhealth-031816-044528
Weiske, C., Petzold, K., & Schad, H. (2015). Multi-Local Living – The Approaches of Rational Choice Theory, Sociology of Everyday Life and Actor-Network Theory. Tijdschrift voor Economische en Sociale Geografie, 106 (4), 392–408. doi:10.1111/tesg.12157
Zhang, D. (2017). Oil shocks and stock markets revisited: Measuring connectedness from a global perspective. Energy Economics, 62 , 323–333. doi:10.1016/j.eneco.2017.01.009
Zhang, D., & Broadstock, D. C. (2020). Global financial crisis and rising connectedness in the international commodity markets. International Review of Financial Analysis, 68 , 101239. doi:10.1016/j.irfa.2018.08.003
Zhang, Z., & Heydon, R. (2016). The changing landscape of literacy curriculum in a Sino-Canada transnational education programme: an actor-network theory informed case study. Journal of Curriculum Studies, 48 (4), 547–564. doi:10.1080/00220272.2015.1090626