Abstract
Political science research frequently models binary or ordered outcomes involving related processes. However, traditional modeling of these outcomes ignores common data issues and cannot capture nuances. There is often an excess of zeros, the observed outcomes for different actors are inherently related, and competing actors may respond to the same factors differently. This paper extends existing models and develops a zero-inflated multivariate ordered probit to simultaneously address these issues. This model performs better than existing models at capturing the true parameters of interest, estimates the nature of the related processes, and captures the differences in actors’ decision-making. I demonstrate these benefits through simulation exercises and an application to party behavior in Mexico.
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