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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. Wilde, Joachim, 2000. "Identification of multiple equation probit models with endogenous dummy regressors," Economics Letters, Elsevier, vol. 69(3), pages 309-312, December.
    2. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    3. Juan Carlos Escanciano & Lin Zhu, 2013. "Set inferences and sensitivity analysis in semiparametric conditionally identified models," CeMMAP working papers 55/13, Institute for Fiscal Studies.
    4. 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.
    5. 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.
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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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