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Inference in Limited Dependent Variable Models Robust to Weak Identification

Author

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  • Leandro M. Magnusson

    (Department of Economics, Tulane University)

Abstract

We propose tests for structural parameters in limited dependent variable models with endogenous explanatory variables using the classical minimum distance framework. These tests have the correct size whether the structural parameters are identified or not. Relating to the current tests, the application of ours is appropriate especially to models whose moment conditions are nonlinear in parameters. Moreover, the computation of ours tests is simple, allowing their implementation in a large number of statistical software packages. We compare our tests with Wald tests by performing simulation experiments. We use our tests to analyze the female labor supply and the demand for cigarette.

Suggested Citation

  • Leandro M. Magnusson, 2008. "Inference in Limited Dependent Variable Models Robust to Weak Identification," Working Papers 0801, Tulane University, Department of Economics, revised Apr 2009.
  • Handle: RePEc:tul:wpaper:0801
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    References listed on IDEAS

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    Cited by:

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    3. Frazier, David T. & Renault, Eric & Zhang, Lina & Zhao, Xueyan, 2021. "Weak Identification in Discrete Choice Models," The Warwick Economics Research Paper Series (TWERPS) 1336, University of Warwick, Department of Economics.
    4. Tetsuya Kaji, 2019. "Theory of Weak Identification in Semiparametric Models," Papers 1908.10478, arXiv.org, revised Aug 2020.
    5. Antonio Diez de Los Rios, 2015. "A New Linear Estimator for Gaussian Dynamic Term Structure Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 282-295, April.
    6. M. Shahe Emran & Fenohasina Maret-Rakotondrazaka & Stephen C. Smith, 2014. "Education and Freedom of Choice: Evidence from Arranged Marriages in Vietnam," Journal of Development Studies, Taylor & Francis Journals, vol. 50(4), pages 481-501, April.
    7. Jean-Marie Dufour & Joachim Wilde, 2018. "Weak identification in probit models with endogenous covariates," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(4), pages 611-631, October.
    8. Dakyung Seong, 2022. "Binary response model with many weak instruments," Papers 2201.04811, arXiv.org, revised Feb 2022.
    9. Fiorini, Luciana C. & Jetter, Michael & Parmeter, Christopher F. & Parsons, Christopher, 2020. "The Effect of Community Size on Electoral Preferences: Evidence From Post-WWII Southern Germany," IZA Discussion Papers 13724, Institute of Labor Economics (IZA).
    10. David T. Frazier & Eric Renault & Lina Zhang & Xueyan Zhao, 2020. "Weak Identification in Discrete Choice Models," Papers 2011.06753, arXiv.org, revised Jan 2021.
    11. Chuhui Li & Donald S Poskitt & Frank Windmeijer & Xueyan Zhao, 2019. "Binary Outcomes, OLS, 2SLS and IV Probit," Monash Econometrics and Business Statistics Working Papers 5/19, Monash University, Department of Econometrics and Business Statistics.
    12. Wendy Correa Martínez & Michael Jetter, 2016. "Isolating causality between gender and corruption: An IV approach," Documentos de Trabajo CIEF 014438, Universidad EAFIT.
    13. Gregory Cox, 2020. "Weak Identification with Bounds in a Class of Minimum Distance Models," Papers 2012.11222, arXiv.org.
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    15. Aparicio, Juan P. & Jetter, Michael, 2020. "Captivating News in Colombia," IZA Discussion Papers 13834, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    weak identification; minimum chi-square estimation; hypothesis testing; limited dependent variable models;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models

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