[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v49y2017i25p2428-2442.html
   My bibliography  Save this article

Predicting the competitive relationships of industrial production between Taiwan and China using Lotka–Volterra model

Author

Listed:
  • Bi-Huei Tsai
Abstract
This work is the first to apply Lotka–Volterra model combined with genetic algorithm (GA) to predict the production relationships of high-tech industry among different areas. Previous studies analysed the trade interdependency among various countries, but few studies have highlighted the quantitative evidence of production relationships. Thus, this study utilizes motherboard shipment volumes to predict the competitive relationships of industrial production on both sides of the Taiwan Strait. Specifically, this work uses simultaneous non-linear least square regression in combination with GAs for numerical parameter optimization of the proposed Lotka–Volterra model. The results of parameter estimation reveal that shipment growth in China substantially promotes that in Taiwan, whereas the shipment growth in Taiwan curtails that in China. The standard deviation of the estimated parameters from the 3000 iterated simulations is small, confirming the reliability and stability of our parameter estimations. According to equilibrium analysis, the results of Lyapunov function prove that the shipments of China and Taiwan will reach a stable long-term equilibrium. The potential production from China will ultimately be nearly 16 times as large as that from Taiwan. Finally, the analytical results of forecast accuracy confirm that Lotka–Volterra model performs better than conventional S-curve diffusion model in predicting motherboard shipments.

Suggested Citation

  • Bi-Huei Tsai, 2017. "Predicting the competitive relationships of industrial production between Taiwan and China using Lotka–Volterra model," Applied Economics, Taylor & Francis Journals, vol. 49(25), pages 2428-2442, May.
    • Handle: RePEc:taf:applec:v:49:y:2017:i:25:p:2428-2442
    DOI: 10.1080/00036846.2016.1240347
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2016.1240347
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2016.1240347?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.

    References listed on IDEAS

    as
    1. Xing, Yuqing, 2012. "Processing trade, exchange rates and China's bilateral trade balances," Journal of Asian Economics, Elsevier, vol. 23(5), pages 540-547.
    2. Tsai, Bi-Huei, 2013. "Predicting the diffusion of LCD TVs by incorporating price in the extended Gompertz model," Technological Forecasting and Social Change, Elsevier, vol. 80(1), pages 106-131.
    3. Wang, Zhi, 2003. "WTO accession, the "Greater China" free-trade area, and economic integration across the Taiwan Strait," China Economic Review, Elsevier, vol. 14(3), pages 316-349.
    4. Chien, Chen-Fu & Chen, Yun-Ju & Peng, Jin-Tang, 2010. "Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle," International Journal of Production Economics, Elsevier, vol. 128(2), pages 496-509, December.
    5. Tsai, Bi-Huei & Chang, Chih-Jen & Chang, Chun-Hsien, 2016. "Elucidating the consumption and CO2 emissions of fossil fuels and low-carbon energy in the United States using Lotka–Volterra models," Energy, Elsevier, vol. 100(C), pages 416-424.
    6. Raymond Vernon, 1966. "International Investment and International Trade in the Product Cycle," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 80(2), pages 190-207.
    7. Christos Papadas & W. George Hutchinson, 2002. "Neural network forecasts of input-output technology," Applied Economics, Taylor & Francis Journals, vol. 34(13), pages 1607-1615.
    8. Yongjing Zhang, 2012. "A Lotka--Volterra evolutionary model of China's incremental institutional reform," Applied Economics Letters, Taylor & Francis Journals, vol. 19(4), pages 367-371, March.
    9. Pascal Seppecher & Isabelle Salle, 2015. "Deleveraging crises and deep recessions: a behavioural approach," Applied Economics, Taylor & Francis Journals, vol. 47(34-35), pages 3771-3790, July.
    10. Yeh, Kuo-chun & Ho, Tai-kuang, 2012. "Magnitude and volatility of Taiwan's net foreign assets against Mainland China: 1981–2009," China Economic Review, Elsevier, vol. 23(3), pages 720-728.
    11. Chia-Hung Sun & Yi-Bin Chiu, 2010. "Taiwan's trade imbalance and exchange rate revisited," Applied Economics, Taylor & Francis Journals, vol. 42(7), pages 917-922.
    12. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    13. Bi-Huei Tsai & Yiming Li, 2011. "Modelling competition in global LCD TV industry," Applied Economics, Taylor & Francis Journals, vol. 43(22), pages 2969-2981.
    14. Tsangyao Chang & Wenshwo Fang & Li-Fang Wen & Chwenchi Liu, 2001. "Defence spending, economic growth and temporal causality: evidence from Taiwan and mainland China, 1952-1995," Applied Economics, Taylor & Francis Journals, vol. 33(10), pages 1289-1299.
    15. Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2005. "A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia," Applied Economics, Taylor & Francis Journals, vol. 37(6), pages 665-680.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Marco Desogus & Beatrice Venturi, 2023. "Stability and Bifurcations in Banks and Small Enterprises—A Three-Dimensional Continuous-Time Dynamical System," JRFM, MDPI, vol. 16(3), pages 1-20, March.
    2. Dingxuan Huang & Claudio O. Delang & Yongjiao Wu & Shuliang Li, 2021. "An Improved Lotka–Volterra Model Using Quantum Game Theory," Mathematics, MDPI, vol. 9(18), pages 1-17, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bi-Huei Tsai & Yao-Min Huang, 2023. "Comparing the Substitution of Nuclear Energy or Renewable Energy for Fossil Fuels between the United States and Africa," Sustainability, MDPI, vol. 15(13), pages 1-16, June.
    2. Qin, Ruwen & Nembhard, David A., 2012. "Demand modeling of stochastic product diffusion over the life cycle," International Journal of Production Economics, Elsevier, vol. 137(2), pages 201-210.
    3. Chumnumpan, Pattarin & Shi, Xiaohui, 2019. "Understanding new products’ market performance using Google Trends," Australasian marketing journal, Elsevier, vol. 27(2), pages 91-103.
    4. Shi, Xiaohui & Chumnumpan, Pattarin, 2019. "Modelling market dynamics of multi-brand and multi-generational products," European Journal of Operational Research, Elsevier, vol. 279(1), pages 199-210.
    5. Sang-Gun Lee & Byeonghwa Park & Si-Hyeon Kim & Hong-Hee Lee, 2012. "Innovation and imitation effects in the mobile telecommunication service market," Service Business, Springer;Pan-Pacific Business Association, vol. 6(3), pages 265-278, September.
    6. Zhu Wang, 2008. "Income Distribution, Market Size and the Evolution of Industry," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 11(3), pages 542-565, July.
    7. Shiva & Neetu Gupta & Anu G. Aggarwal, 2024. "A generalized product adoption model under random marketing conditions," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(10), pages 4897-4904, October.
    8. Guo, Xuezhen, 2014. "A novel Bass-type model for product life cycle quantification using aggregate market data," International Journal of Production Economics, Elsevier, vol. 158(C), pages 208-216.
    9. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2021. "Technology diffusion model with change in adoption rate and repeat purchases: a case of consumer balking," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(1), pages 29-36, February.
    10. Zhu Wang, 2006. "Learning, diffusion and the industry life cycle," Payments System Research Working Paper PSR WP 04-01, Federal Reserve Bank of Kansas City.
    11. Negahban, Ashkan & Smith, Jeffrey S., 2018. "Optimal production-sales policies and entry time for successive generations of new products," International Journal of Production Economics, Elsevier, vol. 199(C), pages 220-232.
    12. Elalem, Yara Kayyali & Maier, Sebastian & Seifert, Ralf W., 2023. "A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1874-1894.
    13. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2020. "Modeling technology diffusion: a study based on market coverage and advertising efforts," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 154-162, July.
    14. Michael Dietrich, 2006. "Neural networks and the evolution of firms and industries: An application to UK SIC34 and SIC72," Working Papers 2006007, The University of Sheffield, Department of Economics, revised May 2006.
    15. Oscar Gutiérrez & Francisco Ruiz-Aliseda, 2011. "Real options with unknown-date events," Annals of Finance, Springer, vol. 7(2), pages 171-198, May.
    16. Popoyan, Lilit & Napoletano, Mauro & Roventini, Andrea, 2017. "Taming macroeconomic instability: Monetary and macro-prudential policy interactions in an agent-based model," Journal of Economic Behavior & Organization, Elsevier, vol. 134(C), pages 117-140.
    17. Shari, Babajide Epe & Dioha, Michael O. & Abraham-Dukuma, Magnus C. & Sobanke, Victor O. & Emodi, Nnaemeka V., 2022. "Clean cooking energy transition in Nigeria: Policy implications for Developing countries," Journal of Policy Modeling, Elsevier, vol. 44(2), pages 319-343.
    18. Iamsiraroj, Sasi, 2016. "The foreign direct investment–economic growth nexus," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 116-133.
    19. Cambier, Adrien & Chardy, Matthieu & Figueiredo, Rosa & Ouorou, Adam & Poss, Michael, 2022. "Optimizing subscriber migrations for a telecommunication operator in uncertain context," European Journal of Operational Research, Elsevier, vol. 298(1), pages 308-321.
    20. Jan Fagerberg & Martin Srholec, 2017. "Global Dynamics, Capabilities and the Crisis," Economic Complexity and Evolution, in: Andreas Pyka & Uwe Cantner (ed.), Foundations of Economic Change, pages 83-106, Springer.

    More about this item

    Statistics

    Access and download statistics

    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:taf:applec:v:49:y:2017:i:25:p:2428-2442. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    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.