Hierarchical Bayesian Small Area Estimation
Description
Functions to compute small area estimates based on a basic area or
unit-level model. The model is fit using restricted maximum likelihood, or
in a hierarchical Bayesian way. In the latter case numerical integration is
used to average over the posterior density for the between-area variance.
The output includes the model fit, small area estimates and corresponding
mean squared errors, as well as some model selection measures. Additional functions
provide means to compute aggregate estimates and mean squared errors, to minimally
adjust the small area estimates to benchmarks at a higher aggregation
level, and to graphically compare different sets of small area estimates.