IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v137y2024ics0140988324005103.html
   My bibliography  Save this article

Exploring resource blessing hypothesis within the coffin of technological innovation and economic risk: Evidence from wavelet quantile regression

Author

Listed:
  • Liu, Lingcai
  • Adebayo, Tomiwa Sunday
  • Hu, Jin
  • Irfan, Muhammad
  • Abbas, Shujaat

Abstract

In contemporary times, the interrelationship between financial development and natural resources remains ambiguous in research findings. Some studies support the concept of a resource curse, while others confirm the idea of a resource blessing. Therefore, we clarify the resource blessing/curse hypothesis by considering the role of technological progress and economic risk. The study employed data spanning from 1985/q1 to 2020/q4. Given the non-linear distribution of the data, we employ innovative wavelet quantile regression. Unlike traditional quantile regression, wavelet quantile regression allows for identifying interrelationships between series across various quantiles and periods. Additionally, we utilized quantile ADF and quantile PP to assess the stationarity characteristics of the series, with quantile-on-quantile regression serving as a robustness check for wavelet quantile regression. The results of wavelet quantile regression indicate a significant positive impact of natural resources on financial development in the long-term, supporting the resource blessing hypothesis. Furthermore, empirical evidence reveals that both technological advancement and economic risk contribute significantly to sustained financial development over the long term. The findings of quantile-on-quantile regression align with the outcomes of wavelet quantile regression, reinforcing the robustness of the results. In light of these results, we put forth policy recommendations.

Suggested Citation

  • Liu, Lingcai & Adebayo, Tomiwa Sunday & Hu, Jin & Irfan, Muhammad & Abbas, Shujaat, 2024. "Exploring resource blessing hypothesis within the coffin of technological innovation and economic risk: Evidence from wavelet quantile regression," Energy Economics, Elsevier, vol. 137(C).
  • Handle: RePEc:eee:eneeco:v:137:y:2024:i:c:s0140988324005103
    DOI: 10.1016/j.eneco.2024.107802
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988324005103
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2024.107802?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.

    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:eee:eneeco:v:137:y:2024:i:c:s0140988324005103. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    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.