Hierarchical Bayesian Model with Inequality Constraints for US County Estimates

Chen Lu (), Nandram Balgobin () and Cruze Nathan B. ()
Additional contact information
Chen Lu: National Institute of Statistical Sciences, 1750 K Street, NW, Suite 1100, Washington. D.C., 20006-2306, U.S.A.
Nandram Balgobin: Worcester Polytechnic Institute and USDA National Agricultural statistics Service, Department of Mathematical Sciences, Stratton Hall, 100 Institute Road, Worcester, MA 01609. U.S.A.
Cruze Nathan B.: USDA National Agricultural statistics Service, 1400 Independence Avenue, SW, Washington, D.C. 20250-2054.

Journal of Official Statistics, 2022, vol. 38, issue 3, 709-732

Abstract: In the production of US agricultural official statistics, certain inequality and benchmarking constraints must be satisfied. For example, available administrative data provide an accurate lower bound for the county-level estimates of planted acres, produced by the U.S. Department of Agriculture’s (USDA) National Agricultural statistics Services (NASS). In addition, the county-level estimates within a state need to add to the state-level estimates. A sub-area hierarchical Bayesian model with inequality constraints to produce county-level estimates that satisfy these important relationships is discussed, along with associated measures of uncertainty. This model combines the County Agricultural Production Survey (CAPS) data with administrative data. Inequality constraints add complexity to fitting the model and present a computational challenge to a full Bayesian approach. To evaluate the inclusion of these constraints, the models with and without inequality constraints were compared using 2014 corn planted acres estimates for three states. The performance of the model with inequality constraints illustrates the improvement of county-level estimates in accuracy and precision while preserving required relationships.

Keywords: Administrative data; bayesian diagnostic; benchmarking; crop acreage estimates; small area estimation; sub-area models; survey data (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/jos-2022-0032 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:38:y:2022:i:3:p:709-732:n:5

DOI: 10.2478/jos-2022-0032

Access Statistics for this article

Journal of Official Statistics is currently edited by Annica Isaksson and Ingegerd Jansson

More articles in Journal of Official Statistics from Sciendo
Bibliographic data for series maintained by Peter Golla ().