IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-22-00330.html
   My bibliography  Save this article

Forecast the inflation rate in Lebanon: The use of the artificial neural networks method

Author

Listed:
  • Jean-François Verne

    (Université Saint-Joseph de Beyrouth, Faculté de Sciences Economiques)

Abstract

In this article, we use the neural network method to analyze the inflation rate evolution in Lebanon between December 2008 to January 2022. We particularly use the Nonlinear Autoregressive model, called NAR, which belongs to the models of Artificial Neural Networks (ANN), to make predictions of the inflation rate in Lebanon over a twelve-month period. Then, we compare the goodness of fit of these predictions from such a model with that obtained by the seasonal ARIMA model. Thus, the NAR model generates better results, in terms of forecasting and adjustment of the estimated data to the observed data than those achieved with the linear seasonal ARIMA model.

Suggested Citation

  • Jean-François Verne, 2022. "Forecast the inflation rate in Lebanon: The use of the artificial neural networks method," Economics Bulletin, AccessEcon, vol. 42(4), pages 1798-1810.
    • Handle: RePEc:ebl:ecbull:eb-22-00330
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2022/Volume42/EB-22-V42-I4-P149.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Inflation rate; Neural Networks; Forecast; Autoregressive models;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

    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:ebl:ecbull:eb-22-00330. 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: John P. Conley (email available below). General contact details of provider: .

    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.