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Weak identification in discrete choice models

Author

Listed:
  • Frazier, David T.
  • Renault, Eric
  • Zhang, Lina
  • Zhao, Xueyan

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.

Suggested Citation

  • Frazier, David T. & Renault, Eric & Zhang, Lina & Zhao, Xueyan, 2025. "Weak identification in discrete choice models," Journal of Econometrics, Elsevier, vol. 248(C).
  • Handle: RePEc:eee:econom:v:248:y:2025:i:c:s0304407624002112
    DOI: 10.1016/j.jeconom.2024.105866
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    More about this item

    Keywords

    Discrete choice models; Weak instruments; Weak identification; Identification testing;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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