Efficient GMM Estimation with Incomplete Data
针对存在任意多个不完全性分层的数据（如非平衡面板、工具变量部分缺失），提出广义矩条件并推导效率界，给出高效估计量，即使每个分层内识别失败也能实现整体识别。
In the standard missing data model, data are either complete or completely missing. However, applied researchers face situations with an arbitrary number of strata of incompleteness. Examples include unbalanced panels and instrumental variables settings where some observations are missing some instruments. I propose a model for settings where observations may be incomplete, with an arbitrary number of strata of incompleteness. I derive a set of moment conditions that generalizes those in Graham's ( 2011 ) standard missing data setup. I derive the associated efficiency bound and propose efficient estimators. Identification can be achieved even if it fails in each stratum of incompleteness.