ANOTHER LOOK AT THE IDENTIFICATION AT INFINITY OF SAMPLE SELECTION MODELS
提出一种新的识别策略，基于结果变量取大值时选择变量与协变量独立的条件，无需协变量的大支撑集，且该条件可检验，适用于广义Roy模型的识别。
It is often believed that without instruments, endogenous sample selection models are identified only if a covariate with a large support is available (see, e.g., Chamberlain, 1986, Journal of Econometrics 32, 189–218; Lewbel, 2007, Journal of Econometrics 141, 777–806) . We propose a new identification strategy mainly based on the condition that the selection variable becomes independent of the covariates for large values of the outcome. No large support on the covariates is required. Moreover, we prove that this condition is testable. We finally show that our strategy can be applied to the identification of generalized Roy models.