Abstract

The Consumer Price Index (CPI) survey is designed to measure inflation by collecting quotes in sampled Core-Based Statistical Areas (CBSA) of the U.S. The current design provides for reliable estimation of relative price changes with uncertainty measures in a limited number of large self-representative (SR) CBSAs and Census Divisions. To produce estimates in other localities (i.e. states), we use area level modeling to mass impute inflation measures in all CBSAs in the U.S. Our project faces multiple challenges, including approximately estimated variances of direct estimates, data sparsity, and sampled CBSAs being poorly representative of the population. We co-model point and variance estimates in small areas to mitigate the effect of unreliably estimated variances, and employ intelligently constructed highly informative priors, data clustering, and spatio-temporal modeling to compensate for sparsity and lack of representativeness of the available sample.