Header

UZH-Logo

Maintenance Infos

Strategy and sample selection: a strategic selection estimator


Leemann, Lucas (2014). Strategy and sample selection: a strategic selection estimator. Political Analysis, 22(3):374-397.

Abstract

The development and proliferation of strategic estimators has narrowed the gap between theoretical models and empirical testing. But despite recent contributions that extend the basic strategic estimator, researchers have continued to neglect a classic social science phenomenon: selection. Compared to nonstrategic estimators, strategic models are even more prone to selection effects. First, external shocks or omitted variables can lead to correlated errors. Second, because the systematic parts of actors' utilities usually overlap on certain key variables, the two sets of explanatory variables are correlated. As a result, both the systematic and the stochastic components can be correlated. However, given that the estimates for the first mover are computed based on the potentially biased predicted probabilities of the second actor, we also generate biased estimates for the first actor. In applied work, researchers neglect the potential shortcomings due to selection bias. This article presents an alternative strategic estimator that takes selection into account and allows scholars to obtain consistent, unbiased, and efficient estimates in the presence of both selection and strategic action. I present a Monte Carlo analysis as well as a real-world application to illustrate the superior performance of this estimator relative to the standard practice.

Abstract

The development and proliferation of strategic estimators has narrowed the gap between theoretical models and empirical testing. But despite recent contributions that extend the basic strategic estimator, researchers have continued to neglect a classic social science phenomenon: selection. Compared to nonstrategic estimators, strategic models are even more prone to selection effects. First, external shocks or omitted variables can lead to correlated errors. Second, because the systematic parts of actors' utilities usually overlap on certain key variables, the two sets of explanatory variables are correlated. As a result, both the systematic and the stochastic components can be correlated. However, given that the estimates for the first mover are computed based on the potentially biased predicted probabilities of the second actor, we also generate biased estimates for the first actor. In applied work, researchers neglect the potential shortcomings due to selection bias. This article presents an alternative strategic estimator that takes selection into account and allows scholars to obtain consistent, unbiased, and efficient estimates in the presence of both selection and strategic action. I present a Monte Carlo analysis as well as a real-world application to illustrate the superior performance of this estimator relative to the standard practice.

Statistics

Citations

Dimensions.ai Metrics
2 citations in Web of Science®
4 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

2 downloads since deposited on 26 Nov 2018
0 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Political Science
Dewey Decimal Classification:320 Political science
Scopus Subject Areas:Social Sciences & Humanities > Sociology and Political Science
Social Sciences & Humanities > Political Science and International Relations
Language:English
Date:2014
Deposited On:26 Nov 2018 17:14
Last Modified:15 Apr 2020 21:53
Publisher:Oxford University Press
ISSN:1047-1987
OA Status:Closed
Publisher DOI:https://doi.org/10.1093/pan/mpt026

Download

Closed Access: Download allowed only for UZH members

Content: Published Version
Language: English
Filetype: PDF - Registered users only
Size: 4MB
View at publisher