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USPD Doubling or Declining in Next Decade Estimated by WASD Neuronet Using Data as of October 2013

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Computational Intelligence and Intelligent Systems (ISICA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 575))

Abstract

Recently, the total public debt outstanding (TPDO) of the United States has increased rapidly, and to more than \(\$17\) trillion on October 18, 2013. It is important and necessary to conduct the TPDO projection for better policies making and more effective measurements taken. In this paper, we present the ten-year projection for the public debt of the United States (termed also the US public debt, USPD) via a 3-layer feed-forward neuronet. Specifically, using the calendar year data on the USPD from the Department of the Treasury, the neuronet is trained, and then is applied to projection. Via a series of numerical tests, we find that there are several possibilities of the change of the USPD in the future, which are classified into two categories in terms of projection trend: the continuous-increase trend and the increase-peak-decline trend. In the most possible situation, the neuronet indicates that the TPDO of the United States is projected to increase, and it will double in 2019 and double again in 2024.

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References

  1. US Government Accountability Office: Federal Debt Basics - How Large is the Federal Debt? Government Accountability Office, Washington, D.C. (2012)

    Google Scholar 

  2. US Government Accountability Office: The Nation’s Long-Term Fiscal Outlook: September 2008 Update. Government Accountability Office, Washington, D.C. (2008)

    Google Scholar 

  3. Friedman, B.M.: Debt and Economic Activity in the United States. University of Chicago Press, Chicago (1982)

    Google Scholar 

  4. US Department of the Treasury: Public Debt Reports: Historical Debt Outstanding - Annual. Department of the Treasury, Washington, D.C. (2013)

    Google Scholar 

  5. US Congressional Budget Office: Updated Budget Projections: Fiscal Years 2013 to 2023. Congressional Budget Office, Washington, D.C. (2013)

    Google Scholar 

  6. US Congressional Budget Office: The 2013 Long-Term Budget Outlook. Congressional Budget Office, Washington, D.C. (2013)

    Google Scholar 

  7. Chernew, M.E., Baicker, K., Hsu, J.: The specter of financial armageddon-health care and federal debt in the united states. New Engl. J. Med. 362(13), 1166–1168 (2010)

    Article  Google Scholar 

  8. Young, J.A.: Technology and competitiveness: a key to the economic future of the united states. Science 241(4863), 313–316 (1998)

    Article  Google Scholar 

  9. Ooms, V.D.: Budget priorities of the nation. Science 258(5089), 1742–1747 (1992)

    Article  Google Scholar 

  10. Sarno, L.: The behavior of US public debt: a nonlinear perspective. Econ. Lett. 74(1), 119–125 (2001)

    Article  MATH  Google Scholar 

  11. Greiner, A., Kauermann, G.: Sustainability of US public debt: estimating smoothing spline regressions. Econ. Modell. 24(2), 350–364 (2007)

    Article  Google Scholar 

  12. Aizenman, J., Marion, N.: Using inflation to erode the US public debt. J. Macroecon. 33(4), 524–541 (2011)

    Article  Google Scholar 

  13. US Office of Management and Budget: The Budget for Fiscal Year 2013. Historical Tables. Washington, D.C. Office of Management and Budget (2013)

    Google Scholar 

  14. Celasun, O., Keim, G.: The U.S. Federal Debt Outlook: Reading the Tea Leaves. IMF Working Paper, International Monetary Fund, United States (2010)

    Google Scholar 

  15. Cecchetti, S.G., Mohanty, M.S., Zampolli, F.: The Future of Public Debt: Prospects and Implications. Bank for International Settlements, Monetary and Economic Department (2010)

    Google Scholar 

  16. Meuhleisen, M., Towe, C.M., Cardarelli, R.: United States Fiscal Policies and Priorities for Long-Run Sustainability. International Monetary Fund, United States (2004)

    Google Scholar 

  17. Bohn, H.: The behavior of U.S public debt and deficits. Q. J. Econ. 113(3), 949–963 (1998)

    Article  Google Scholar 

  18. Zhang, Y., Yu, X., Xiao, L., Li, W., Fan, Z.: Weights and Structure Determination of Artificial Neuronets (Chapter 5). Nova Science Publishers, New York (2013)

    Google Scholar 

  19. Zhang, Y., Chen, Y., Jiang, X., Zeng, Q., Zou, A.: Weights-directly-determined and structure-adaptively-tuned neural network based on chebyshev basis functions. Comput. Sci. 36, 210–213 (2009)

    Google Scholar 

  20. Ferone, A., Maddalena, L.: Neural background subtraction for pan-tilt-zoom cameras. IEEE Trans. Syst. Man Cybern. Syst. 44(5), 571–579 (2014)

    Article  Google Scholar 

  21. Henriquez, P., Alonso, J.B., Ferrer, M.A., Travieso, C.M.: Review of automatic fault diagnosis systems using audio and vibration signals. IEEE Trans. Syst. Man Cybern. Syst. 44(5), 642–652 (2014)

    Article  Google Scholar 

  22. Ge, S.S., Yang, C., Lee, T.H.: Adaptive predictive control using neural network for a class of pure-feedback systems in discrete time. IEEE Trans. Neural Netw. 19(9), 1599–1614 (2008)

    Article  Google Scholar 

  23. Wang, Z., Liu, Y., Li, M., Liu, X.: Stability analysis for stochastic cohen-grossberg neural networks with mixed time delays. IEEE Trans. Neural Netw. 17(3), 814–820 (2006)

    Article  Google Scholar 

  24. Kabir, H., Wang, Y., Yu, M., Zhang, Q.: High-dimensional neural-network technique and applications to microwave filter modeling. IEEE Trans. Microw. Theory Tech. 58(1), 145–156 (2010)

    Article  Google Scholar 

  25. Liang, X., Wang, J.: A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints. IEEE Trans. Neural Netw. 11(6), 1251–1262 (2000)

    Article  Google Scholar 

  26. US Department of the Treasury: Public Debt Reports: Monthly Statement of the Public Debt (MSPD) and Downloadable Files. Department of the Treasury, Washington, D.C. (2013)

    Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (with number 61473323), by the Foundation of Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, China (with number 2013A07), and also by the Science and Technology Program of Guangzhou, China (with number 2014J4100057).

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Authors and Affiliations

  1. School of Information Science and Technology, Sun Yat-sen University, Guangzhou, 510006, China

    Yunong Zhang, Zhengli Xiao, Dongsheng Guo, Mingzhi Mao & Hongzhou Tan

  2. SYSU-CMU Shunde International Joint Research Institute, Shunde, 528300, China

    Yunong Zhang, Zhengli Xiao, Dongsheng Guo & Hongzhou Tan

  3. Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Guangzhou, 510640, China

    Yunong Zhang, Zhengli Xiao & Dongsheng Guo

Authors
  1. Yunong Zhang
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  2. Zhengli Xiao
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  3. Dongsheng Guo
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  4. Mingzhi Mao
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  5. Hongzhou Tan
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Corresponding author

Correspondence to Yunong Zhang .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, The South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. School of Computer Science, Guangzhou University, Guangzhou, China

    Jin Li

  3. School of Computer Science and Engineeri, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

  4. Dept. of Informatics, University of Salerno, Fisciano, Italy

    Aniello Castiglione

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© 2016 Springer Science+Business Media Singapore

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Zhang, Y., Xiao, Z., Guo, D., Mao, M., Tan, H. (2016). USPD Doubling or Declining in Next Decade Estimated by WASD Neuronet Using Data as of October 2013. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_75

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  • DOI: https://doi.org/10.1007/978-981-10-0356-1_75

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0355-4

  • Online ISBN: 978-981-10-0356-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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