The Household Pulse Survey (HPS), recently released by the U.S. Census Bureau, gathers timely information about the societal and economic impacts of coronavirus. The first phase of the survey was quickly launched one month after the beginning of the coronavirus pandemic and ran for 12 weeks. To track the immediate impact of the pandemic, individual respondents during this phase were re-sampled for up to three consecutive weeks. Motivated by expected job loss during the pandemic, using public-use microdata, this work proposes unit-level, model-based estimators that incorporate longitudinal dependence at both the response and domain level. In particular, using a pseudo-likelihood, we consider a Bayesian hierarchical unit-level, model-based approach for both Gaussian and binary response data under informative sampling. To facilitate construction of these model-based estimates, we develop an efficient Gibbs sampler. An empirical simulation study is conducted to compare the proposed approach to models that do not account for unit-level longitudinal correlation. Finally, using public-use HPS micro-data, we provide an analysis of "expected job loss" that compares both design-based and model-based estimators and demonstrates superior performance for the proposed model-based approaches.

翻译：家庭脉搏调查（HPS）近期由美国人口普查局发布，旨在收集关于新冠病毒对社会和经济影响的及时信息。该调查的第一阶段在新冠疫情开始一个月后迅速启动，并持续了12周。为追踪疫情的即时影响，此阶段中的受访者被重新抽样最多连续三周。基于疫情期间预期失业这一动机，本研究利用公开微观数据，提出了在响应层面和领域层面均纳入纵向依赖性的单元级模型驱动估计量。具体而言，我们采用伪似然方法，针对高斯型和二元型响应数据，在信息抽样下构建了贝叶斯分层单元级模型驱动方法。为便于构建这些模型驱动估计量，我们开发了高效的吉布斯采样器。通过模拟仿真研究，我们将所提方法与未考虑单元级纵向相关性的模型进行了比较。最后，利用公开的HPS微观数据，我们对"预期失业"进行了分析，比较了基于设计和基于模型的估计量，并证明了所提模型驱动方法的优越性能。