序列非单调缺失的双稳健GMM估计量
A Doubly Robust GMM Estimator for Sequential Non-monotone Missingness
AI总结：
针对两个序列收集变量存在非单调缺失的问题，提出基于序列随机缺失假设的增广逆概率加权GMM估计量，实现双稳健性和半参数有效界，并在俄勒冈健康保险实验数据中显著降低标准误。
摘要：
我们研究当两个序列收集的变量存在非单调缺失时的矩估计。常用的随机缺失（MAR）假设要求所有缺失机制依赖于相同的完全观测协变量，但在这种情况下往往不成立。我们引入了一个序列MAR假设，允许不同阶段的缺失机制不对称。基于该假设，我们构建了一个增广逆概率加权GMM（AIPW-GMM）估计量。该估计量具有增广项的不对称结构，保证了双稳健性，并达到了闭式半参数有效界。应用于俄勒冈健康保险实验的两期调查数据，支持了新假设的可观测含义。所提出的方法将俄勒冈健康计划对老年人影响的估计标准误降低了50%以上，使得先前统计上不显著的估计变得显著。
英文摘要：
We study moment-based estimation with two sequentially collected variables subject to non-monotone missingness. The commonly used Missing at Random (MAR) assumption requiring all missingness mechanisms to depend on the same fully observed covariates often fails in such cases. We introduce a sequential MAR assumption that allows asymmetric missingness mechanisms across stages. Based on this assumption, we construct an Augmented Inverse-Probability-Weighted GMM (AIPW-GMM) estimator. The estimator features an asymmetric structure for the augmentation term, guarantees double robustness, and achieves the closed-form semiparametric efficiency bound. An application to two-period survey data from the Oregon Health Insurance Experiment supports the observable implications of the new assumption. The proposed approach reduces the standard errors by more than 50% for the estimated effects of the Oregon Health Plan among older adults, "driving" previously statistically insignificant estimates significant.
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