Mark Nieman
Department of Political Science 
Iowa State University

 Strategic Binary Choice Models with Partial Observability

Strategic interactions among rational, self-interested actors are commonly theorized in the behavioral, economic, and social sciences. The theorized strategic processes have traditionally been modeled with multi-stage structural estimators, which improve parameter estimates at one stage by using the information from other stages. Multi-stage approaches, however, impose rather strict demands on data availability: data must be available for the actions of each strategic actor at every stage of the interaction. Observational data are not always structured in a manner that is conducive to these approaches. Moreover, the theorized strategic process implies that these data are missing not at random. I derive an estimator that probabilistically estimates unobserved actor choices related to earlier stages of strategic interactions. I compare the estimator to traditional logit/probit and split-population logit/probit estimators using Monte Carlo simulations. I apply the estimator to two empirical applications: firm-regulation interactions regarding environmental policy, and rebel-government interactions associated with the onset of civil war.

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