IDEAS home Printed from https://ideas.repec.org/a/ids/ijbisy/v43y2023i4p542-552.html
   My bibliography  Save this article

Comparison between artificial neural network and linear model prediction performance for FDI disparity and the growth rate of companies in Hungarian counties

Author

Listed:
  • Devesh Singh

Abstract

Artificial neural network (ANN) is a growing concept in economics. The objective of this article is to compare the prediction performance of FDI inflow in companies and the growth rate of the companies in Hungarian counties through linear and ANN model. This article examines the regional level data of GDP per capita, urbanisation, market share of foreign investment received companies, labour productivity and agglomeration over the period of 2001 to 2018. Further, this article used the RStudio platform to analyse the data with resilient backpropagation with weight backtracking algorithm and root mean square error to check the predictability performance between ANN and linear model. The result suggests that ANN has an ability to converge, generalised and learns the growth rate of companies and FDI disparities for forecasting. The scatter plot of performance comparison depicts, ANN framework has better efficiency of forecast compared to the linear model.

Suggested Citation

  • Devesh Singh, 2023. "Comparison between artificial neural network and linear model prediction performance for FDI disparity and the growth rate of companies in Hungarian counties," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 43(4), pages 542-552.
    • Handle: RePEc:ids:ijbisy:v:43:y:2023:i:4:p:542-552
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=132806
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:ids:ijbisy:v:43:y:2023:i:4:p:542-552. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=172 .

    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.