IDENTIFICATION AND INFERENCE ON REGRESSIONS WITH MISSING COVARIATE DATA
协变量缺失回归模型的识别与推断
This paper examines the problem of identification and inference on a conditional moment condition model with missing data, with special focus on the case when the conditioning covariates are missing. We impose no assumption on the distribution of the missing data and we confront the missing data problem by using a worst case scenario approach. We characterize the sharp identified set and argue that this set is usually too complex to compute or to use for inference. Given…