IDEAS home Printed from https://ideas.repec.org/a/sae/jocore/v57y2013i3p410-444.html
   My bibliography  Save this article

Prediction of Intrastate Conflict Using State Structural Factors and Events Data

Author

Listed:
  • Peter Tikuisis

    (DRDC Toronto, Defence Research and Development Canada, Toronto, Ontario, Canada)

  • David Carment

    (Carleton University, Ottawa, Ontario, Canada)

  • Yiagadeesen Samy

    (Carleton University, Ottawa, Ontario, Canada)

Abstract

The primary objective of this article is to advance the development of early warning of intrastate conflict by combining country-level structural and events data in a logistic regression model calibrated and validated using split-sample cases. Intrastate conflict is defined by the occurrence of one or more highly destabilizing events collectively termed a crisis of interest (COI). Two separate two-year periods between 1990 and 2005 were examined in twenty-five globally dispersed countries. COIs occurred in about 6 percent of all the half-monthly periods examined. While model accuracy (total correct predictions of COI and non-COI) usually exceeded 90 percent, the model did not generate sufficiently high and consistent precision (correct number of COI over total predicted) and recall (correct number of COI over total observed) for practical use.

Suggested Citation

  • Peter Tikuisis & David Carment & Yiagadeesen Samy, 2013. "Prediction of Intrastate Conflict Using State Structural Factors and Events Data," Journal of Conflict Resolution, Peace Science Society (International), vol. 57(3), pages 410-444, June.
    • Handle: RePEc:sae:jocore:v:57:y:2013:i:3:p:410-444
    as

    Download full text from publisher

    File URL: http://jcr.sagepub.com/content/57/3/410.abstract
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Charles Butcher & Benjamin E. Goldsmith & Sascha Nanlohy & Arcot Sowmya & David Muchlinski, 2020. "Introducing the Targeted Mass Killing Data Set for the Study and Forecasting of Mass Atrocities," Journal of Conflict Resolution, Peace Science Society (International), vol. 64(7-8), pages 1524-1547, August.
    2. Robert A. Blair & Nicholas Sambanis, 2020. "Forecasting Civil Wars: Theory and Structure in an Age of “Big Data†and Machine Learning," Journal of Conflict Resolution, Peace Science Society (International), vol. 64(10), pages 1885-1915, November.

    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:sae:jocore:v:57:y:2013:i:3:p:410-444. 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: SAGE Publications (email available below). General contact details of provider: http://pss.la.psu.edu/ .

    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.