发表机构
Toyo University（东洋大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用刷新样本识别面板条件效应，提出尖锐边界与设计假设，并通过日本面板数据验证，为处理面板流失提供新方法。
AI 中文摘要
刷新样本是处理面板流失的标准方法，其背后的识别结果假定参与不会改变测量。我们刻画了当流失不受限制时，刷新样本能够识别关于面板条件效应的内容，该效应被建模为再访时的确定性单调映射。一个候选映射与数据一致当且仅当留存者隐含潜在结果的留存规模调整分布在集合意义上被刷新分布所支配；每个这样的映射都能被一个明确的流失过程合理化。在没有流失的情况下，该映射在潜在结果支撑集上被识别；存在流失时，一个密度比条件控制识别集，且仅凭尾部行为不能确定它。对于无限制映射，幸存者的平均效应具有熟悉的修整边界；对于所有类别均被报告的条目，模型简化为对无条件效应的检验。在同一队列内，其他波次、退出模式和进入波次条目不会改变识别集，除非限制跨波次连接选择。在明确的选择限制下，生存匹配、对称匹配和进入波次校正识别幸存者效应。我们推导了它们的偏差，并给出了刷新计划识别条件效应路径曲率的秩条件。一个日本面板数据说明了这些结果。
英文摘要
Refreshment samples are the standard remedy for panel attrition, and the identification results behind them maintain that participation does not change measurement. We characterize what a refreshment sample identifies about panel conditioning, modelled as a deterministic monotone map at reinterview, when attrition is unrestricted. A candidate map is consistent with the data if and only if the retention-scaled distribution of the stayers' implied latent outcomes is setwise dominated by the refreshment distribution; every such map is rationalized by an explicit attrition process. Without attrition the map is identified on the latent-outcome support; with attrition, a density-ratio condition governs the identified set, and tail behaviour alone does not determine it. For an unrestricted map the survivors' mean effect has the familiar trimming bounds; for an item with all categories reported, the model reduces to a test of no conditioning. Within a cohort, other waves, dropout patterns and entry-wave items leave the set unchanged unless restrictions link selection across waves. Under explicit selection restrictions, survival matching, symmetric matching and entry-wave correction identify survivor effects. We derive their biases and give rank conditions under which refreshment schedules identify curvature in the conditioning path. A Japanese panel illustrates the results.
Comments89 pages, 1 figure. Companion to arXiv:2609.28871. Replication archive: https://github.com/sokubo/paper-refreshment-designs-replication (tag paper-v1.5)
