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	Duration Models in Politics	Event Data Analysis
分野	Political Science	Political Science
系統	Regression model	Process / pipeline
提唱年	1972	—
提唱者	David R. Cox (Cox model); popularized in political science by Janet Box-Steffensmeier & Bradford Jones	Conflict-studies and computational-social-science traditions (McClelland, Schrodt, King)
種類	Time-to-event regression model	Automated coding and analysis of who-did-what-to-whom event records
原典	Box-Steffensmeier, J. M., & Jones, B. S. (2004). Event History Modeling: A Guide for Social Scientists. Cambridge University Press. ISBN: 9780521546737	Schrodt, P. A. (2012). Precedents, Progress, and Prospects in Political Event Data. International Interactions, 38(4), 546–569.
別名	Event history models, Survival models in political science, Hazard models, Time-to-event models in politics	Event data coding, Political event data, Conflict event data, CAMEO event coding
関連	3	3
概要	Duration models — also called event history or survival models — analyze the time until a political event occurs: how long a cabinet lasts before it falls, how long a war runs before it ends, how long a policy takes to be adopted, or how long a regime survives. Rather than asking only whether an event happens, these models ask when, modeling the hazard rate as a function of covariates while correctly handling censored cases that have not yet experienced the event. The Cox proportional hazards model and parametric alternatives such as the Weibull, popularized in political science by Box-Steffensmeier and Jones, form the core toolkit.	Event data analysis converts streams of news reports into structured records of political interactions — who did what to whom, when — and aggregates them into time series of cooperation and conflict between actors. Each event is coded as a source actor, an action type drawn from an ontology such as CAMEO, a target actor, and a date. Modern systems extract these events automatically from millions of news stories, enabling near-real-time measurement of interstate and intrastate behavior for forecasting and analysis.