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A note on the identification in two equations probit model with dummy endogenous regressor

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  • Mourifié, Ismael
  • Méango, Romuald

Abstract

This paper deals with the question whether exclusion restrictions on the exogenous regressors are necessary to identify two equation probit models with endogenous dummy regressor. We show that Wilde (2000)’s criterion is insufficient for (point) identification.

Suggested Citation

  • Mourifié, Ismael & Méango, Romuald, 2014. "A note on the identification in two equations probit model with dummy endogenous regressor," Economics Letters, Elsevier, vol. 125(3), pages 360-363.
    • Handle: RePEc:eee:ecolet:v:125:y:2014:i:3:p:360-363
    DOI: 10.1016/j.econlet.2014.10.006
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    References listed on IDEAS

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    1. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    2. Juan Carlos Escanciano & Lin Zhu, 2013. "Set inferences and sensitivity analysis in semiparametric conditionally identified models," CeMMAP working papers CWP55/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Sartori, Anne E., 2003. "An Estimator for Some Binary-Outcome Selection Models Without Exclusion Restrictions," Political Analysis, Cambridge University Press, vol. 11(2), pages 111-138, April.
    4. Wilde, Joachim, 2000. "Identification of multiple equation probit models with endogenous dummy regressors," Economics Letters, Elsevier, vol. 69(3), pages 309-312, December.
    5. Sukjin Han & Edward J. Vytlacil, 2013. "Identification in a Generalization of Bivariate Probit Models with Endogenous Regressors," Department of Economics Working Papers 130908, The University of Texas at Austin, Department of Economics.
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    Cited by:

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    4. Sukjin Han & Sungwon Lee, 2019. "Estimation in a generalization of bivariate probit models with dummy endogenous regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 994-1015, September.
    5. Lina Zhang & David T. Frazier & Don S. Poskitt & Xueyan Zhao, 2020. "Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects," Monash Econometrics and Business Statistics Working Papers 34/20, Monash University, Department of Econometrics and Business Statistics.
    6. Zhou Xun, 2015. "Preference for Redistribution and Inequality Perception in China: Evidence from the CGSS 2006," AMSE Working Papers 1518, Aix-Marseille School of Economics, France.
    7. Li, Chuhui & Poskitt, D.S. & Zhao, Xueyan, 2019. "The bivariate probit model, maximum likelihood estimation, pseudo true parameters and partial identification," Journal of Econometrics, Elsevier, vol. 209(1), pages 94-113.
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    More about this item

    Keywords

    Probit model; Endogenous dummy regressor; Partial identification;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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