The term "Competing Risks" describes duration models in which spells may terminate via multiple outcomes: the term of a cabinet, for example, may end with or without an election; wars persist until the loss or victory of the aggressor. Analysis typically assume stochastic independence among risks, the duration modeling equivalent of independence of irrelevant alternatives. However, many political examples violate this assumption. I review competing risks as a latent variables approach. After discussing methods for modeling dependence that place restrictions on the nature of association, I introduce a parametric generalized dependent risks model in which inter-risk correlation may be estimated and its significance tested. The method employs risk-specific random effects drawn from a multivariate normal distribution. Estimation is conducted using numerical methods and/or Bayesian simulation. Monte Carlo simulation reveals desirable large sample properties of the estimator. Finally, I examine two applications using data on cabinet survival and legislative position taking.
ASJC Scopus subject areas
- Sociology and Political Science
- Political Science and International Relations