A common problem in models for dichotomous dependent variables is “separation,” which occurs when one or more of a model's covariates perfectly predict some binary outcome. Separation raises a particularly difficult set of issues, often forcing researchers to choose between omitting clearly important covariates and undertaking post—hoc data or estimation corrections. In this article I present a method for solving the separation problem, based on a penalized likelihood correction to the standard binomial GLM score function. I then apply this method to data from an important study on the postwar fate of leaders.
An important feature of most political events is their repeatability: nearly all political events reoccur, and theories of learning, path dependence, and institutional change all suggest that later events will differ from earlier ones. Yet, most models for event history analysis fail to account for repeated events, a fact that can yield misleading results in practice. We present a class of duration models for analyzing repeated events, discuss their properties and implementation, and offer recommendations for their use by applied researchers. We illustrate these methods through an application to widely used data on international conflict.
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