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.

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.

Copyright Owner

Institute of Statistical Science, Academia Sinica

Language

en

File Format

application/pdf

Published Version

Share

COinS