发表机构
Center for Economic and Social Research, University of Southern California（南加州大学经济社会研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对存在样本选择的面板数据，利用补充样本，基于逆概率加权与替代加权的分布差构建统计检验，验证可观测选择假设，经模拟和UAS数据集实证验证了其有效性。
AI 中文摘要
在存在样本选择（可能因损耗、无应答等原因产生）的面板数据中，尽管可观测选择（随机缺失，MAR）假设通常不合理，但仍被普遍采用。然而，当存在补充样本时，该假设变得可检验。我们基于两种估计分布的距离构建了MAR的统计检验：一种是在MAR下有效的标准逆概率加权（IPW）得到的分布，另一种是在Hirano等人（2001）提出的较弱可加不可忽略假设下有效的替代加权得到的分布。该检验隐含比较损耗期IPW加权样本的分布与补充样本的分布，若MAR假设成立，二者分布一致。我们证明，当输入分布为参数型时，在MAR原假设下，我们的检验统计量收敛于广义卡方分布，该极限分布可使用Franguridi等人（2026）推导的递推公式估计。我们在蒙特卡洛模拟中展示了该检验的性能，最后将其应用于理解美国研究（UAS）数据集的一个子样本的实证案例。
英文摘要
In panels with sample selection (that may occur due to attrition, nonresponse, etc.), the assumption of selection on observables (missing at random, MAR) is commonly imposed despite often being implausible. However, this assumption becomes testable when a refreshment sample is available. We develop a statistical test of MAR based on a distance between two estimated distributions: one obtained using the standard inverse probability weighting (IPW) that is valid under MAR and the other obtained using an alternative weighting that is valid under a weaker assumption of additive nonignorability of Hirano et al. (2001). This test implicitly compares the distribution of the IPW-weighted sample in the attrition period with the distribution of the refreshment sample, which coincide if the MAR assumption holds. We establish that, when the input distributions are parametric, our test statistic converges to the generalized chi-squared distribution under the null of MAR. This limit distribution can be estimated using the recursive formulas derived by Franguridi et al. (2026). We illustrate the performance of our test in Monte Carlo simulations. Finally, we apply our test to an empirical example using a subsample of the Understanding America Study (UAS) dataset.
