IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v28y2021i16p1363-1370.html
   My bibliography  Save this article

Forecasting macroeconomy using Granger-causality network connectedness

Author

Listed:
  • Dan Wang
  • Wei-Qiang Huang

Abstract

The connectedness among financial institutions reflects potential channels for risk contagion and the amplification of risk to the financial system that can also propagate into the real economy. This study investigates the predictive power of financial network connectedness for macroeconomy. We highlight the connectedness by quantifying the effects of risk transmission among financial institutions in Granger-causality networks. The aggregate macroeconomy is viewed as a proxy for economic activity and is extracted from several monthly single macroeconomic variables by principal component analysis. We use the n-month-ahead multivariate predictive regressions to explore the predictive power of the connectedness and test whether the predictive ability is robust. The results show that after controlling for a number of factors, an increase in network connectedness among Chinese financial institutions strongly and stably predicts higher Chinese economic activity about four to twelve (except for five) months into the future.

Suggested Citation

  • Dan Wang & Wei-Qiang Huang, 2021. "Forecasting macroeconomy using Granger-causality network connectedness," Applied Economics Letters, Taylor & Francis Journals, vol. 28(16), pages 1363-1370, September.
    • Handle: RePEc:taf:apeclt:v:28:y:2021:i:16:p:1363-1370
    DOI: 10.1080/13504851.2020.1817302
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13504851.2020.1817302
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13504851.2020.1817302?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.

    Citations

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


    Cited by:

    1. Wang Yijun & Zhang Yu & Usman Bashir, 2023. "Impact of COVID-19 on the contagion effect of risks in the banking industry: based on transfer entropy and social network analysis method," Risk Management, Palgrave Macmillan, vol. 25(2), pages 1-41, June.

    More about this item

    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:taf:apeclt:v:28:y:2021:i:16:p:1363-1370. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    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.