IDENTIFICATION AND INFERENCE ON REGRESSIONS WITH MISSING COVARIATE DATA
研究了协变量缺失时条件矩模型的识别与推断问题，采用最坏情况方法，提出两种易于计算且具有识别力的外部识别集构建策略。
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 this difficulty, we consider the construction of outer identified sets (i.e. supersets of the identified set) that are easier to compute and can still characterize the parameter of interest. Two different outer identification strategies are proposed. Both of these strategies are shown to have nontrivial identifying power and are relatively easy to use and combine for inferential purposes.