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 never identified even if the errors are known to have the logistic distribution. A test is provided to check the sign saturation condition and can be implemented using existing algorithms for the maximum score estimator. We also discuss the practical implication of our results.

翻译：我们研究确定具有固定效果的二进制选择模型。 我们提供一种称为符号饱和度的条件, 并显示该条件足以识别模型。 特别是, 我们甚至可以保证与受约束的递减者进行识别。 我们还表明, 没有这一条件,即使已知错误有后勤分布, 也永远无法识别模型。 提供测试以检查符号饱和度条件, 并且可以使用现有算法对最高评分算符实施。 我们还讨论我们结果的实际影响 。