We study the identification of binary choice models with fixed effects. We provide a condition called sign saturation and show that this condition is sufficient for the identification of the model. In particular, we can guarantee identification even with bounded regressors. We also show that without this condition, the model is not identified unless the error distribution belongs to a small class. The same sign saturation condition is also essential for identifying the sign of treatment effects. A test is provided to check the sign saturation condition and can be implemented using existing algorithms for the maximum score estimator.

翻译：我们研究了带有固定效应的二元选择模型的识别问题。我们提出了一个称为符号饱和的条件，并证明该条件足以保证模型的识别。特别地，即使回归变量有界，我们也能保证识别性。我们还表明，若不满足此条件，除非误差分布属于一个很小的类别，否则模型无法被识别。相同的符号饱和条件对于识别处理效应的符号也至关重要。我们提供了一个检验方法来验证符号饱和条件，该方法可利用最大得分估计量的现有算法实现。