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Inference in limited dependent variable models robust to weak identification

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  • Leandro M. Magnusson

Abstract

We propose tests for structural parameters in limited dependent variable models with endogenous explanatory variables. These tests are based upon the generalized minimum distance principle. They are of the correct size regardless of whether the structural parameters are identified and are especially appropriate for models whose moment conditions are non-linear in the parameters. Moreover, they are computationally simple, allowing them to be implemented using a large number of statistical software packages. We compare our tests to Wald tests in a simulation experiment and use them to analyse the female labour supply and the demand for cigarettes. Copyright (C) 2010 The Author(s). The Econometrics Journal (C) 2010 Royal Economic Society

Suggested Citation

  • Leandro M. Magnusson, 2010. "Inference in limited dependent variable models robust to weak identification," Econometrics Journal, Royal Economic Society, vol. 13(3), pages 56-79, October.
    • Handle: RePEc:ect:emjrnl:v:13:y:2010:i:3:p:s56-s79
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    Cited by:

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    3. David T. Frazier & Eric Renault & Lina Zhang & Xueyan Zhao, 2020. "Weak Identification in Discrete Choice Models," Papers 2011.06753, arXiv.org, revised Jan 2021.
    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. Chuhui Li & Donald S. Poskitt & Frank Windmeijer & Xueyan Zhao, 2022. "Binary outcomes, OLS, 2SLS and IV probit," Econometric Reviews, Taylor & Francis Journals, vol. 41(8), pages 859-876, September.
    7. 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.
    8. 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.
    9. Luke Barber & Michael Jetter & Tim Krieger, 2023. "Foreshadowing Mars: Religiosity and Pre-Enlightenment Warfare," CESifo Working Paper Series 10806, CESifo.
    10. Dakyung Seong, 2022. "Binary response model with many weak instruments," Papers 2201.04811, arXiv.org, revised May 2023.
    11. 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).
    12. Jetter, Michael & Walker, Jay K., 2022. "News coverage and mass shootings in the US," European Economic Review, Elsevier, vol. 148(C).
    13. Wendy Correa Martínez & Michael Jetter, 2016. "Isolating causality between gender and corruption: An IV approach," Documentos de Trabajo de Valor Público 14438, Universidad EAFIT.
    14. Gregory Cox, 2020. "Weak Identification with Bounds in a Class of Minimum Distance Models," Papers 2012.11222, arXiv.org, revised Dec 2022.
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    16. Aparicio, Juan P. & Jetter, Michael, 2020. "Captivating News in Colombia," IZA Discussion Papers 13834, Institute of Labor Economics (IZA).
    17. Aparicio, Juan P. & Jetter, Michael, 2022. "Captivating news: Media attention and FARC kidnappings," Journal of Economic Behavior & Organization, Elsevier, vol. 202(C), pages 69-81.

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    More about this item

    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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