Identification in a Generalization of Bivariate Probit Models with 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, 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.
|Date of creation:||Sep 2013|
|Contact details of provider:|| Postal: Austin, Texas 78712|
Phone: +1 (512) 471-3211
Fax: +1 (512) 471-3510
Web page: http://www.utexas.edu/cola/depts/economics/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:tex:wpaper:130908. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Caroline Thomas)
If references are entirely missing, you can add them using this form.