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Identification in a Generalization of Bivariate Probit Models with Endogenous Regressors

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Author Info

  • Sukjin Han

    ()
    (Department of Economics, University of Texas at Austin)

  • Edward J. Vytlacil

    ()
    (Department of Economics, New York University)

Abstract

This paper provides identification results for a class of models specified by a triangular system of two equations with binary endogenous variables. The joint distribution of the latent error terms is specified through a parametric copula structure, including the normal copula as a special case, while the marginal distributions of the latent error terms are allowed to be arbitrary but known. This class of models includes bivariate probit models as a special case. The paper demonstrates that an exclusion restriction is necessary and sufficient for globally identification of the model parameters with the excluded variable allowed to be binary. Based on this result, identification is achieved in a full model where common exogenous regressors that are present in both equations and excluded instruments are possibly more general than discretely distributed.

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File URL: https://sites.google.com/site/universs01/mypdf/ID_BVP_Sep4_2013.pdf
File Function: First version, 2013
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Bibliographic Info

Paper provided by The University of Texas at Austin, Department of Economics in its series Department of Economics Working Papers with number 130908.

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Length: 18 pages
Date of creation: Sep 2013
Date of revision:
Handle: RePEc:tex:wpaper:130908

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Keywords: Identification; triangular threshold crossing model; bivariate probit model; endogenous variables; binary response; copula; exclusion restriction;

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