IDEAS home Printed from https://ideas.repec.org/p/unu/wpaper/wp-2023-79.html
   My bibliography  Save this paper

A deep learning approach to estimation of the Phillips curve in South Africa

Author

Listed:
  • Gideon du Rand
  • Hylton Hollander
  • Dawie van Lill

Abstract

In this study, we provide a comprehensive estimation of the contemporary Phillips curve relationship in the South African economy using a novel deep learning technique. Our approach incorporates multiple measures of economic slack/tightness and inflation expectations, contributing to the debate on the relevance of the Phillips curve in South Africa, where previous findings have been inconclusive. Our analysis reveals that long-run inflation expectations are the primary driver of inflation, with these expectations anchored around 5% historically but declining since the financial crisis.

Suggested Citation

  • Gideon du Rand & Hylton Hollander & Dawie van Lill, 2023. "A deep learning approach to estimation of the Phillips curve in South Africa," WIDER Working Paper Series wp-2023-79, World Institute for Development Economic Research (UNU-WIDER).
    • Handle: RePEc:unu:wpaper:wp-2023-79
    as

    Download full text from publisher

    File URL: https://www.wider.unu.edu/sites/default/files/Publications/Working-paper/PDF/wp2023-79-deep-learning-approach-estimation-Phillips-curve-South-Africa.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Monique B. Reid & Pierre L. Siklos, 2022. "How Firms and Experts View The Phillips Curve: Evidence from Individual and Aggregate Data from South Africa," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 58(12), pages 3355-3376, September.
    Full references (including those not matched with items on IDEAS)

    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. Monique Reid & Pierre Siklos, 2023. "Rationality and biases insights from disaggregated firm level inflation expectations data," Working Papers 11050, South African Reserve Bank.
    2. Monique Reid & Pierre Siklos, 2024. "Firm level expectations and macroeconomic conditions underpinnings and disagreement," Working Papers 11058, South African Reserve Bank.
    3. Chris Loewald & Konstantin Makrelov & Ekaterina Pirozhkova, 2022. "TheshorttermcostsofreducingtrendinflationinSouthAfrica," Working Papers 11029, South African Reserve Bank.

    More about this item

    Keywords

    Inflation; Output gap; Monetary policy;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:unu:wpaper:wp-2023-79. 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: Siméon Rapin (email available below). General contact details of provider: https://edirc.repec.org/data/widerfi.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.