Campus Units
Political Science
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
10-2018
Journal or Book Title
Statistica Sinica
Volume
28
Issue
4
First Page
2089
Last Page
2105
DOI
10.5705/ss.202016.0294
Abstract
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. In this paper, I derive a strategic logistic regression model with partial observability that probabilistically estimates unobserved actor choices related to earlier stages of strategic interactions. I compare the estimator to traditional logit and split-population logit estimators using Monte Carlo simulations and a substantive example of the strategic firm-regulator interaction associated with pollution and environmental sanctions.
Copyright Owner
Institute of Statistical Science, Academia Sinica
Copyright Date
2018
Language
en
File Format
application/pdf
Recommended Citation
Nieman, Mark David, "Strategic Binary Choice Models with Partial Observability" (2018). Political Science Publications. 58.
https://lib.dr.iastate.edu/pols_pubs/58
Included in
Databases and Information Systems Commons, Political Theory Commons, Strategic Management Policy Commons
Comments
This article is published as Nieman, M.D., Strategic Binary Choice Models with Partial Observability. Statistics Sinica. October 2018, 28(4); 2089-2105. doi: 10.5705/ss.202016.0294. Posted with permission.