Bayesian Unit-level Small Area Estimation Modeling of Longitudinal Survey Data under Informative Sampling

Music City Center 

Unit-level models, which model survey responses directly, offer a number of advantages over area-level models, which model aggregated estimates. However, accounting for a complex survey design becomes more challenging in the unit-level setting. In particular, little work has been done to extend such models to capture longitudinal designs, where temporal correlation exists at both the response and domain level. We consider a Bayesian hierarchical unit-level, model-based approach that handles Gaussian, binary, and categorical data, incorporates longitudinal dependence and multiscale time series structure, and accounts for informative sampling. To handle computational scalability, we develop an efficient Gibbs sampler with appropriate data augmentation. 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 microdata, we provide an analysis of the Household Pulse Survey that compares both design-based and model-based estimators and demonstrates superior performance for the proposed approaches.

Bayesian

Informative Sampling

Longitudinal

Small Area Estimation

Unit-level