国家层面与领域层面：调查重新设计下分层贝叶斯可信区间的覆盖特性
National Versus Domain: Coverage Properties of HB Credible Intervals Under Survey Redesign
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中文总结 AI 辅助
研究在四种压力测试场景下考察国家和领域层面95%分层贝叶斯可信区间的频率覆盖，涵盖多个分层和领域，测试恢复领域覆盖策略，发现国家层面覆盖良好，领域层面部分不足，罕见事件场景下HB估计器表现优于经典直接估计器，特定校正策略无效。
中文摘要 AI 辅助
Tam（2026）的一篇配套论文报告了一项扩展的蒙特卡罗（MC）研究，该研究在四种压力测试场景下，考察了国家和领域层面95%分层贝叶斯（HB）可信区间的频率覆盖情况。扩展研究分别涵盖140个分层和13个估计领域，增加了经典直接估计器基准，并测试了两种恢复领域覆盖的策略：先验敏感性和Prasad与Rao（PR）均方误差校正。在国家层面，HB可信区间在所有四种场景和所有三个劳动力变量中实现了接近名义覆盖。在领域层面，工作时长覆盖接近名义。在罕见事件场景（D）中，经典直接估计器在就业和工作时长方面国家覆盖降至0%，而HB估计器以约15%的经典样本成本实现了96%至100%的国家覆盖。较弱先验或PR均方误差校正都不能可靠恢复领域覆盖。
英文摘要
A companion paper to Tam (2026) reports an extended Monte Carlo (MC) study examining the frequentist coverage of 95% hierarchical Bayes (HB) credible intervals at the national and domain levels under four stress-test scenarios. The extended study covers 140 strata and 13 estimation domains separately, adds a classical direct-estimator benchmark, and tests two strategies for restoring domain coverage: prior sensitivity and Prasad and Rao (PR) MSE correction. At the national level, HB credible intervals achieve near-nominal coverage across all four scenarios and all three labour force variables (Employment 93 to 96%, Unemployment 87 to 97%, Hours Worked 99.5 to 100%). At the domain level, Hours Worked coverage is nearnominal (94 to 98%) in all scenarios; Employment and Unemployment coverage is below nominal for scenarios with low between-domain heterogeneity, a direct consequence of HB shrinkage toward the national mean. A key operational finding emerges from the Rare Event scenario (D): the classical direct estimator collapses to 0% national coverage for Employment and Hours Worked because five unsampled strata introduce a systematic bias; the HB estimator achieves 96 to 100% national coverage at roughly 15% of the classical sample cost. Neither a weaker prior nor PR MSE correction reliably restores domain coverage, confirming that the failure is bias-driven and cannot be remedied by variance inflation alone.
