IDEAS home Printed from https://ideas.repec.org/a/taf/ginixx/v48y2022i4p649-677.html
   My bibliography  Save this article

Forecasting change in conflict fatalities with dynamic elastic net

Author

Listed:
  • Fulvio Attinà
  • Marcello Carammia
  • Stefano M. Iacus

Abstract

This article illustrates an approach to forecasting change in conflict fatalities designed to address the complexity of the drivers and processes of armed conflicts. The design of this approach is based on two main choices. First, to account for the specificity of conflict drivers and processes over time and space, we model conflicts in each individual country separately. Second, we draw on an adaptive model—Dynamic Elastic Net, DynENet—which is able to efficiently select relevant predictors among a large set of covariates. We include over 700 variables in our models, adding event data on top of the data features provided by the convenors of the forecasting competition. We show that our approach is suitable and computationally efficient enough to address the complexity of conflict dynamics. Moreover, the adaptive nature of our model brings a significant added value. Because for each country our model only selects the variables that are relevant to predict conflict intensity, the retained predictors can be analyzed to describe the dynamic configuration of conflict drivers both across countries and within countries over time. Countries can then be clustered to observe the emergence of broader patterns related to correlates of conflict. In this sense, our approach produces interpretable forecasts, addressing one key limitation of contemporary approaches to forecasting.

Suggested Citation

  • Fulvio Attinà & Marcello Carammia & Stefano M. Iacus, 2022. "Forecasting change in conflict fatalities with dynamic elastic net," International Interactions, Taylor & Francis Journals, vol. 48(4), pages 649-677, July.
  • Handle: RePEc:taf:ginixx:v:48:y:2022:i:4:p:649-677
    DOI: 10.1080/03050629.2022.2090934
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03050629.2022.2090934
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/03050629.2022.2090934?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. Racek, Daniel & Thurner, Paul W. & Davidson, Brittany I. & Zhu, Xiao Xiang & Kauermann, Göran, 2024. "Conflict forecasting using remote sensing data: An application to the Syrian civil war," International Journal of Forecasting, Elsevier, vol. 40(1), pages 373-391.

    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:ginixx:v:48:y:2022:i:4:p:649-677. 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/GINI20 .

    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.