339 – Official Statistics and Small Area Estimation
Mitigating Standard Errors of County-Level Survey Estimates When Data are Sparse
Valbona Bejleri
USDA National Agricultural Statistics Service
Habtamu K. Benecha
USDA National Agricultural Statistics Service (NASS)
Andreea L. Erciulescu
National Institute of Statistical Sciences
Nathan B. Cruze
USDA National Agricultural Statistics Service
Balgobin Nandram
Worcester Polytechnic Institute, Worcester, Massachusetts
The USDA National Agricultural Statistics Service's (NASS's) official statistics at the county level are composites of survey and non-survey data that are manually benchmarked to state and national official estimates. NASS is currently developing Bayesian hierarchical models as an alternative to produce county official statistics using survey summaries and auxiliary data as covariates. The modeled county estimates are linear combinations of survey summaries and auxiliary data, with coefficients depending on the standard errors of direct survey estimates. With this approach, the auxiliary data are not used to produce the final model estimate when the standard error of the direct survey estimate is zero. In this paper, it is shown how to mitigate estimated standard errors of zero. The relationship between the direct survey estimates and their standard errors is modeled, if a relationship between the two is present. Exploratory data analysis is conducted and a data driven distribution-based technique using bootstrapping is proposed for cases where the relationship between estimates and their standard errors cannot be modeled well. An illustration of the method using NASS's County Agricultural Production Survey data is presented.