Forecasting Civil Wars: Theory and Structure in an Age of “Big Data†and Machine Learning

Robert A. Blair and Nicholas Sambanis

Journal of Conflict Resolution, 2020, vol. 64, issue 10, 1885-1915

Abstract: Does theory contribute to forecasting accuracy? We use event data to show that a parsimonious model grounded in prominent theories of conflict escalation can forecast civil war onset with high accuracy and over shorter temporal windows than has generally been possible. Our forecasting model draws on “procedural†variables, building on insights from the contentious politics literature. We show that a procedural model outperforms more inductive, atheoretical alternatives and also outperforms models based on countries’ structural characteristics, which previously dominated models of civil war onset. We find that process can substitute for structure over short forecasting windows. We also find a more direct connection between theory and forecasting than is sometimes assumed, though we suggest that future researchers treat the value-added of theory for prediction not as an assumption but rather as a hypothesis to test.

Keywords: forecasting; civil wars; event data; machine learning (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0022002720918923 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:jocore:v:64:y:2020:i:10:p:1885-1915

DOI: 10.1177/0022002720918923

Access Statistics for this article

More articles in Journal of Conflict Resolution from Peace Science Society (International)
Bibliographic data for series maintained by SAGE Publications ().