<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Hierarchical Bayesian Model with Inequality Constraints for US County Estimates

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Hierarchical Bayesian Model with Inequality Constraints for US County Estimates"}

In the production of US agricultural official statistics, certain inequality and benchmarking constraints must be satisfied. For example, available...

Hierarchical Bayesian Model with Inequality Constraints for US County Estimates

Data publikacji: 12 wrz 2022

Zakres stron: 709 - 732

Otrzymano: 01 sty 2021

Przyjęty: 01 sty 2022

DOI: https://doi.org/10.2478/jos-2022-0032

Słowa kluczowe
Administrative data, bayesian diagnostic, benchmarking, crop acreage estimates, small area estimation, sub-area models, survey data

© 2022 Lu Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Hierarchical Bayesian Model with Inequality Constraints for US County Estimates

Data publikacji: 12 wrz 2022

Zakres stron: 709 - 732

Otrzymano: 01 sty 2021

Przyjęty: 01 sty 2022

DOI: https://doi.org/10.2478/jos-2022-0032

Słowa kluczoweAdministrative data, bayesian diagnostic, benchmarking, crop acreage estimates, small area estimation, sub-area models, survey data

© 2022 Lu Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Administrative data, bayesian diagnostic, benchmarking, crop acreage estimates, small area estimation, sub-area models, survey data