Open Access
Issue
SHS Web Conf.
Volume 73, 2020
Innovative Economic Symposium 2019 – Potential of Eurasian Economic Union (IES2019)
Article Number 01027
Number of page(s) 18
Section Potential of the Eurasian Economic Union
DOI https://doi.org/10.1051/shsconf/20207301027
Published online 13 January 2020
  1. Z. Rowland, P. Šuleř, M. Vochozka, Comparison of neural networks and regression time series in estimating the Czech Republic and China trade balance. SHS Web of Conferences: Innovative Economic Symposium 2018 – Milestones and Trends of the World Economy, 61 (2019) [Google Scholar]
  2. H. Yaping, S. Pengfei, M. Vochozka, Pollution caused by finance and the relative policy analysis in China. Energy & Environment, 28(7), 808-823 (2017) [CrossRef] [Google Scholar]
  3. M. Vochozka, J. Vrbka, Estimation of the development of the Euro to Chinese Yuan exchange rate using artificial neural networks. SHS Web of Conferences: Innovative Economic Symposium 2018 – Milestones and Trends of World Economy, 61 (2019) [Google Scholar]
  4. Understanding the US - China Trade Relationship: Prepared fot the US - China Business Council by Oxford Economics. The US - China Business Council: Understanding the US-China Trade Relationship [online], Available at: https://www.uschina.org/sites/default/files/Oxford%20Economics%20US%20Jobs%20 and%20China%20Trade%20Report.pdf (2017) [Google Scholar]
  5. M. Chossudovsky, Trump’s Trade War with China: Imagine What Would Happen if China Decided to Impose Economic Sanctions on the USA? Global Research: Centre for Research on Globalization [online], Available at: https://www.globalresearch.ca/imagine-what-would-happen-if-china-decided-to- impose-economic-sanctions-on-the-usa-2/5598941 (2017) [Google Scholar]
  6. M. Vochozka, J. Horák, P. Šuleř, Equalizing seasonal time series using artificial neural networks in predicting the Euro-Yuan exchange rate. Journal of Risk and Financial Management, 12(2) (2019) [CrossRef] [Google Scholar]
  7. V.V. Mikheev, S.A. Lukonin, China - USA: multiple vector OF “trade war”. World Economy and International Relations, 63(5),57-66 (2019) [CrossRef] [Google Scholar]
  8. J. Vrbka, Z. Rowland, P. Šuleř, Comparison of neural networks and regression time series in estimating the development of the EU and the PRC trade balance. SHS Web of Conferences: Innovative Economic Symposium 2018 – Milestones and Trends of World Economy, 61 (2019) [Google Scholar]
  9. L.D. Qiu, C. Zhan, X. Wei, An analysis of the China - US trade war through the lens of the trade literature. Economic and Political Studies, 7(2), 148-168 (2019) [CrossRef] [Google Scholar]
  10. M. Yu, Introduction to the Special Issue on Understanding the Current China-U.S. ‘Trade War’. China Economic Journal, 12(2), 97-99 (2019) [CrossRef] [Google Scholar]
  11. E.L.C. Lai, The US - China trade war, the American public opinions and its effects on China. Economic and Political Studies, 7(2), 169-184 (2019) [CrossRef] [Google Scholar]
  12. K. Itakura, Evaluating the Impact of the US - China Trade War. Asian Economic Policy Review (2019) [Google Scholar]
  13. T.T.L. Chong, X. Li, Understanding the China - US trade war: causes, economic impact, and the worst - case scenario. Economic and Political Studies, 7(2), 185-202 (2019) [CrossRef] [Google Scholar]
  14. V. Machová, J. Mareček, Estimation of the development of Czech Koruna to Chinese Yuan exchange rate using artificial neural networks. SHS Web of Conferences: Innovative Economic Symposium 2018 – Milestones and Trends of World Economy, 61 (2019) [Google Scholar]
  15. Y. Li, US Economic Sanctions Against China: A Cultural Explanation of Sanction Effectiveness. Asian Perspective, 38(2), 311-335 (2014) [CrossRef] [Google Scholar]
  16. J. Yang, H. Askari, J. Forrer, H. Teegen, US Economic Sanctions Against China: Who Gets Hurt? World Economy, 27(7), 1047-1081 (2004) [CrossRef] [Google Scholar]
  17. China vows to impose sanctions on US firms supplying Taiwan military. The Guardian [online], Available at: https://www.theguardian.com/world/2019/jul/12/china-taiwan- sanctions-us-firms-military-sales (2019) [Google Scholar]
  18. S.V. Nozdrev, China in Global Financial System. World Economy and International Relations, 60(10), 29-40 (2016) [CrossRef] [Google Scholar]
  19. B. Seyoum, US foreign trade zones and import intensity examining determinants of import intensity in US foreign trade zones. European Business Review, 29(1), 103-122 (2017) [CrossRef] [Google Scholar]
  20. A. Pandey, US - China trade war: Huawei’s loss is Samsung’s gain. DW: Made for minds [online], Available at: https://www.dw.com/en/us-china-trade-war-huaweis-loss- is-samsungs-gain/a-48814477 (2019) [Google Scholar]
  21. T. Besedeš, T.J. Prusa, The Hazardous Effects of Antidumping. Economic Inquiry, 55(1), 9-30 (2017) [CrossRef] [Google Scholar]
  22. H. Lee, Estimating the Protection Effects of Antidumping Duties. Journal of Korea Trade, 13(3), 1-23 (2009) [Google Scholar]
  23. S. Park, The Trade Depressing and Trade Diversion Effects of Antidumping Actions: The Case of China. China Economic Review, 20(3), 542-548 (2009) [CrossRef] [Google Scholar]
  24. N. Choi, Did Anti-dumping Duties Really Restrict Import? Empirical Evidence from the US, the EU, China, and India. East Asian Economic Review, 21(1), 3-27 (2017) [CrossRef] [Google Scholar]
  25. L. Yu, S. Wang, K.K. Lai, Forecasting China’s Foreign Trade Volume with a Kernel-Based Hybrid Econometric-Ai Ensemble Learning Approach. Journal of Systems Science and Complexity, 21(1), 1-19 (2008) [CrossRef] [Google Scholar]
  26. J. Deggans, T. Krulický, M. Kováčová, K. Valášková, M. Polliak, Cognitively enhanced products, output growth and labor market changes: Will artificial intelligence replace workers by automating their jobs? Economics, Management, and Financial Markets, 14(1), 38-43 (2019) [CrossRef] [Google Scholar]
  27. J. Weijin, X. Yuhui, A novel method for nonlinear time series forecasting of time - delay neural network. Wuhan University Journal of Natural Sciences, 11(5), 1357-1361 (2006) [CrossRef] [Google Scholar]
  28. World Bank [online], Available at: https://data.worldbank.org/indicator/TM.TAX.MRCH.WM.AR.ZS?end=2017&locatio ns=CN-US&start=2014 (2019) [Google Scholar]

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