Weak identification in discrete choice models

David T. Frazier, Eric Renault, Lina Zhang and Xueyan Zhao

Journal of Econometrics, 2025, vol. 248, issue C

Abstract: We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, we propose a novel test that can consistently detect weak identification in commonly applied discrete choice models, such as probit, logit, and many of their extensions. Furthermore, we demonstrate that when the null hypothesis of weak identification is rejected, Wald-based inference can be carried out using standard formulas and critical values. A Monte Carlo study compares our proposed testing approach against commonly applied weak identification tests. The results simultaneously demonstrate the good performance of our approach and the fundamental failure of using conventional weak identification tests for linear models in the discrete choice model context. Lastly, we apply our approach in two empirical examples: married women labor force participation, and US food aid and civil conflicts.

Keywords: Discrete choice models; Weak instruments; Weak identification; Identification testing (search for similar items in EconPapers)
JEL-codes: C12 C26 C35 C36 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:248:y:2025:i:c:s0304407624002112

DOI: 10.1016/j.jeconom.2024.105866

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