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：我们研究了具有固定效应的二元选择模型的识别问题。我们提出了一种称为“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，我们也能确保模型的识别。我们还证明，若缺乏该条件，除非误差分布属于一个较小的类别，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。我们提供了一种检验符号饱和条件的方法，该方法可利用现有的最大值评分估计算法实现。