WITHDRAW: Spatio-temporal Bayesian modeling for estimating CPIs in small domains´

Music City Center 

There is interest in estimating Consumer Price Indexes (CPI) for small Core-Based Statistical Areas (CBSAs) and states. Currently, consumer prices are sampled in select CBSAs with the goal of providing reliable index estimates at the national-level, Census division-level, and for CBSAs with sufficiently large populations. We use hierarchical Bayesian models and incorporate covariates and spatio-temporal correlations of consumer prices with the idea that accounting for these correlations will compensate for the sparseness of the collected data and will allow for reliable predictions in the small areas. Our research presented at 2024 JSM demonstrated the utility of accounting for spatial correlations. We are currently investigating if a series of temporal estimates in CBSAs will compensate for the sparseness of direct cross-sectional estimates. We check our model assumptions by comparing estimated and predicted fuel prices with estimates from large administrative datasets.

small area estimation

hierarchical Bayesian models

spatio-temporal correlations

STAN

Gaussian processes 

Survey Research Methods Section