Identification in a Generalization of Bivariate Probit Models with Endogenous Regressors
Identification in a Generalization of Bivariate Probit Models with Endogenous Regressors
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具有内生回归量的二变量概率模型推广中的识别
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
E. Vytlacil
中科院分区：
文献类型：
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作者：
Sukjin Han;E. Vytlacil
本文给出了一类二元内生变量三角方程组模型的辨识结果。潜在误差项的联合分布通过一个参数Copula结构来指定，该结构满足与一阶随机优势度相关的特定依赖排序，而边际分布允许是任意的，但已知。这类模型是广泛的，包括双变量概率单位模型作为一种特殊情况。本文证明了在没有共同外生协变量的模型中，具有排除限制是全局识别的必要和充分条件，其中允许排除的变量是二进制的。在两个方程中都存在共同外生协变量的模型中，具有排除限制是足够的，但不是必要的。
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 that is related to the degree of the first-order stochastic dominance, 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 globally identification in a model without common exogenous covariates, where the excluded variable is allowed to be binary. Having an exclusion restriction is sufficient but not necessary in models with common exogenous covariates that are present in both equations.