IDEAS home Printed from https://ideas.repec.org/a/taf/jnlbes/v40y2022i1p216-226.html
   My bibliography  Save this article

Community Detection in Partial Correlation Network Models

Author

Listed:
  • Christian Brownlees
  • Guðmundur Stefán Guðmundsson
  • Gábor Lugosi

Abstract

We introduce a class of partial correlation network models with a community structure for large panels of time series. In the model, the series are partitioned into latent groups such that correlation is higher within groups than between them. We then propose an algorithm that allows one to detect the communities using the eigenvectors of the sample covariance matrix. We study the properties of the procedure and establish its consistency. The methodology is used to study real activity clustering in the United States.

Suggested Citation

  • Christian Brownlees & Guðmundur Stefán Guðmundsson & Gábor Lugosi, 2022. "Community Detection in Partial Correlation Network Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 216-226, January.
    • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:1:p:216-226
    DOI: 10.1080/07350015.2020.1798241
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07350015.2020.1798241
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/07350015.2020.1798241?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. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.

    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:jnlbes:v:40:y:2022:i:1:p:216-226. 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/UBES20 .

    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.