Identification in a generalization of bivariate probit models with dummy endogenous regressors

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 that satisfies a particular dependence ordering, wh...

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Bibliographic Details
Published in:Journal of econometrics 2017-07, Vol.199 (1), p.63-73
Main Authors: Han, Sukjin, Vytlacil, Edward J.
Format: Article
Language:English
Subjects:
Binary response
Bivariate probit model
Copula
Dummy
Dummy endogenous regressors
Endogenous
Error analysis
Exclusion restriction
Human error
Identification
Marginal analysis
Regression analysis
Studies
Triangular threshold crossing model
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Summary: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 that satisfies a particular dependence ordering, while the marginal distributions are allowed to be arbitrary but known. This class of models is broad and includes bivariate probit models as a special case. The paper demonstrates that having an exclusion restriction is necessary and sufficient for global identification in a model without common exogenous covariates, where the excluded variable is allowed to be binary. Having an exclusion restriction is sufficient in models with common exogenous covariates that are present in both equations. The paper then extends the identification analyses to a model where the marginal distributions of the error terms are unknown.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2017.04.001