IDEAS home Printed from https://ideas.repec.org/h/spr/adschp/978-3-031-15149-1_8.html
   My bibliography  Save this book chapter

Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance

In: Econometrics with Machine Learning

Author

Listed:
  • Ekaterina Seregina

    (Colby College)

Abstract

Many economic and financial systems, including financial markets, financial institutions, and macroeconomic policy making can be modelled as systems of interacting agents. Graphical models, which are the main focus of this chapter, are a means of estimating the relationships implied by such systems. The main goals of this chapter are (1) acquainting the readers with graphical models; (2) reviewing the existing research on graphical models for economic and finance problems; (3) reviewing the literature that merges graphical models with other machine learning methods in economics and finance.

Suggested Citation

  • Ekaterina Seregina, 2022. "Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 251-290, Springer.
    • Handle: RePEc:spr:adschp:978-3-031-15149-1_8
    DOI: 10.1007/978-3-031-15149-1_8
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:adschp:978-3-031-15149-1_8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.