IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v80y2013i8p1557-1562.html
   My bibliography  Save this article

Long-term GDP forecasts and the prospects for growth

Author

Listed:
  • Modis, Theodore

Abstract

The growth of GDP is considered as a natural-growth process amenable to description by the logistic-growth equation. The S-shaped logistic pattern provides good descriptions and forecasts for both nominal and real GDP per capita in the US over the last 80years. This enables the calculation of a long-term forecast for inflation, which is to enter a declining trend not so far in the future. The two logistics are well advanced, more so for nominal GDP. The assumption for logistic growth works even better for Japan whose nominal GDP per capita has already completed tracing out an entire logistic trajectory. The economic woes of industrialized countries could be attributed to the saturation of growth there, as if a niche in nature had been filled to capacity. In contrast, GDP growth in China and India is in the very early stages of logistic growth still indistinguishable from exponential patterns. The ceiling of these logistics can be anywhere between 5 and 10 times today's levels.

Suggested Citation

  • Modis, Theodore, 2013. "Long-term GDP forecasts and the prospects for growth," Technological Forecasting and Social Change, Elsevier, vol. 80(8), pages 1557-1562.
    • Handle: RePEc:eee:tefoso:v:80:y:2013:i:8:p:1557-1562
    DOI: 10.1016/j.techfore.2013.02.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S004016251300036X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2013.02.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dušan Marković & Igor Mladenović & Miloš Milovančević, 2017. "RETRACTED ARTICLE: Estimation of the most influential science and technology factors for economic growth forecasting by soft computing technique," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1133-1146, May.
    2. Sokolov-Mladenović, Svetlana & Milovančević, Milos & Mladenović, Igor, 2017. "Evaluation of trade influence on economic growth rate by computational intelligence approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 358-362.
    3. Petra Karanikić & Igor Mladenović & Svetlana Sokolov-Mladenović & Meysam Alizamir, 2017. "RETRACTED ARTICLE: Prediction of economic growth by extreme learning approach based on science and technology transfer," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1395-1401, May.
    4. Marković, Dušan & Petković, Dalibor & Nikolić, Vlastimir & Milovančević, Miloš & Petković, Biljana, 2017. "Soft computing prediction of economic growth based in science and technology factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 217-220.
    5. Đokić, Aleksandar & Jović, Srđan, 2017. "Evaluation of agriculture and industry effect on economic health by ANFIS approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 396-399.
    6. Sinem Kilic Celik & M. Ayhan Kose & Franziska Ohnsorge, 2023. "Potential Growth Prospects: Risks, Rewards and Policies," CAMA Working Papers 2023-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Kordanuli, Bojana & Barjaktarović, Lidija & Jeremić, Ljiljana & Alizamir, Meysam, 2017. "Appraisal of artificial neural network for forecasting of economic parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 515-519.
    8. Schlembach, Christoph & Schmidt, Sascha L. & Schreyer, Dominik & Wunderlich, Linus, 2022. "Forecasting the Olympic medal distribution – A socioeconomic machine learning model," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    9. Su, Yu-Shan & Huang, Hsini & Daim, Tugrul & Chien, Pan-Wei & Peng, Ru-Ling & Karaman Akgul, Arzu, 2023. "Assessing the technological trajectory of 5G-V2X autonomous driving inventions: Use of patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    10. Milačić, Ljubiša & Jović, Srđan & Vujović, Tanja & Miljković, Jovica, 2017. "Application of artificial neural network with extreme learning machine for economic growth estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 285-288.
    11. de Groot, E.A. & Segers, R. & Prins, D., 2021. "Disentangling the enigma of multi-structured economic cycles - A new appearance of the golden ratio," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    12. Jović, Srđan & Maksimović, Goran & Jovović, David, 2016. "Appraisal of natural resources rents and economic development," Resources Policy, Elsevier, vol. 50(C), pages 289-291.
    13. Igor Mladenović & Miloš Milovančević & Svetlana Sokolov-Mladenović, 2017. "RETRACTED ARTICLE: Analyzing of innovations influence on economic growth by fuzzy system," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1297-1304, May.
    14. Maksimović, Goran & Jović, Srđan & Jovanović, Radomir, 2017. "Economic growth rate management by soft computing approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 520-524.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:80:y:2013:i:8:p:1557-1562. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.