IDEAS home Printed from https://ideas.repec.org/p/phd/dpaper/dp_2018-33.html
   My bibliography  Save this paper

Exploring Neural Network Models in Understanding Bilateral Trade in APEC: A Review of History and Concepts

Author

Listed:
  • Quimba, Francis Mark A.
  • Barral, Mark Anthony A.

Abstract

This paper seeks to understand certain frameworks that can be used to improve the analysis and prediction of trade flows within the Asia-Pacific Economic Cooperation economies using neural networks. Discussions include the history of neural network development, the biological neuron, the artificial neuron, and the potential use of neural networks in trade analysis. This paper also compares the different estimation procedures of the gravity model–-Ordinary Least Squares, Poisson Pseudo Maximum Likelihood, and Gamma Pseudo Maximum Likelihood-–with the neural network. Study finds that the neural network estimation of the gravity equation is superior over the other procedures in terms of explaining the variability of the dependent variable (export) around its mean and in terms of the accuracy of predictions.

Suggested Citation

  • Quimba, Francis Mark A. & Barral, Mark Anthony A., 2018. "Exploring Neural Network Models in Understanding Bilateral Trade in APEC: A Review of History and Concepts," Discussion Papers DP 2018-33, Philippine Institute for Development Studies.
    • Handle: RePEc:phd:dpaper:dp_2018-33
    as

    Download full text from publisher

    File URL: https://www.pids.gov.ph/publication/discussion-papers/exploring-neural-network-models-in-understanding-bilateral-trade-in-apec-a-review-of-history-and-concepts
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Jošić Hrvoje & Žmuk Berislav, 2022. "A Machine Learning Approach to Forecast International Trade: The Case of Croatia," Business Systems Research, Sciendo, vol. 13(3), pages 144-160, October.
    2. Simon Blöthner & Mario Larch, 2022. "Economic determinants of regional trade agreements revisited using machine learning," Empirical Economics, Springer, vol. 63(4), pages 1771-1807, October.

    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:phd:dpaper:dp_2018-33. 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: Aniceto Orbeta (email available below). General contact details of provider: https://edirc.repec.org/data/pidgvph.html .

    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.