Identification in a generalization of bivariate probit models with dummy endogenous regressors. (English) Zbl 1452.62916
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.
MSC:
| 62P20 | Applications of statistics to economics |
| 62J05 | Linear regression; mixed models |
| 62G05 | Nonparametric estimation |
| 62H05 | Characterization and structure theory for multivariate probability distributions; copulas |
Keywords:
identification; triangular threshold crossing model; bivariate probit model; dummy endogenous regressors; binary response; copula; exclusion restrictionSoftware:
SemiParBIVProbitReferences:
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