Benchmarking a triplet of official estimates

Erciulescu, Andreea L.; Cruze, Nathan B.; Nandram, Balgobin

doi:10.1007/s10651-018-0416-4

Cited by 8 publications

(8 citation statements)

References 7 publications

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“…Second, the yield, as the ratio of production to harvested acres, needs to aggregate to the corresponding state-level estimates. The study by Erciulescu et al [19] explored the preservation of triplet relationships among the numerator totals, denominator totals, and their ratios for two nested, smaller-than-state geographies.…”

Section: Small Area Models For Crop County Estimatesmentioning

confidence: 99%

“…Chen et al [21] examined the performance of the model with inequality constraints and, through a case study, illustrated the improvement in the county-level estimates in terms of accuracy and precision while preserving the required relationships. Erciulescu et al [19] discussed the yield model and different methods of applying benchmarking constraints to a triplet (numerator, denominator, ratio) and illustrated results for 2014 for corn and soybeans in Indiana, Iowa, and Illinois. Based on these results, small area models implemented in crop county estimates for total acre and yield estimates provide accurate indirect estimates while improving the precision.…”

Section: Small Area Models For Crop County Estimatesmentioning

confidence: 99%

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Using Small Area Estimation to Produce Official Statistics

Young

Chen

2022

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The USDA National Agricultural Statistics Service (NASS) and other federal statistical agencies have used probability-based surveys as the foundation for official statistics for over half a century. Non-survey data that can be used to improve the accuracy and precision of estimates such as administrative, remotely sensed, and retail data have become increasingly available. Both frequentist and Bayesian models are used to combine survey and non-survey data in a principled manner. NASS has recently adopted Bayesian subarea models for three of its national programs: farm labor, crop county estimates, and cash rent county estimates. Each program provides valuable estimates at multiple scales of geography. For each program, technical challenges had to be met and a strenuous review completed before models could be adopted as the foundation for official statistics. Moving models out of the research phase into production required major changes in the production process and a cultural shift. With the implemented models, NASS now has measures of uncertainty, transparency, and reproducibility of its official statistics.

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Section: Small Area Models For Crop County Estimatesmentioning

confidence: 99%

Section: Small Area Models For Crop County Estimatesmentioning

confidence: 99%

Using Small Area Estimation to Produce Official Statistics

Young

Chen

2022

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“…Yield is defined as the ratio of total production to the harvested acreage. Starting in 2020, several HB subarea-level (small area) models have been implemented as extensions of the FH model to improve the precision of the estimates at the county level (see [2][3][4][5][6]). The sampling variances of the yield estimates are produced using a second-order Taylor series approximation and, due to various reasons (e.g., sparseness in data), could result in zero, very small or very large estimated variances for several counties.…”

Section: Introductionmentioning

confidence: 99%

Smoothing County-Level Sampling Variances to Improve Small Area Models’ Outputs

Chen

Sartore

Benecha

et al. 2022

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The use of hierarchical Bayesian small area models, which take survey estimates along with auxiliary data as input to produce official statistics, has increased in recent years. Survey estimates for small domains are usually unreliable due to small sample sizes, and the corresponding sampling variances can also be imprecise and unreliable. This affects the performance of the model (i.e., the model will not produce an estimate or will produce a low-quality modeled estimate), which results in a reduced number of official statistics published by a government agency. To mitigate the unreliable sampling variances, these survey-estimated variances are typically modeled against the direct estimates wherever a relationship between the two is present. However, this is not always the case. This paper explores different alternatives to mitigate the unreliable (beyond some threshold) sampling variances. A Bayesian approach under the area-level model set-up and a distribution-free technique based on bootstrap sampling are proposed to update the survey data. An application to the county-level corn yield data from the County Agricultural Production Survey of the United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) is used to illustrate the proposed approaches. The final county-level model-based estimates for small area domains, produced based on updated survey data from each method, are compared with county-level model-based estimates produced based on the original survey data and the official statistics published in 2016.

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“…For estimating domain means of households ratios of food expenditure, as well as for other nonlinear bivariate parameters, the statistical literature presents few model‐based contributions. For covering this gap, Erciulescu et al (2018) gave an interesting proposal. They discussed some methods of applying benchmarking constraints to a triplet (numerator, denominator, ratio), at multiple stages of aggregation, where the denominator and the ratio are modeled and the numerator is derived.…”

Section: Introductionmentioning

confidence: 99%

Empirical best prediction of small area bivariate parameters

Esteban

Lombardía

López-Vizcaíno³

et al. 2022

Scandinavian J Statistics

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This paper introduces empirical best predictors of small area bivariate parameters, like ratios of sums or sums of ratios, by assuming that the target unit-level vector follows a bivariate nested error regression model. The corresponding means squared errors are estimated by parametric bootstrap. Several simulation experiments empirically study the behavior of the introduced statistical methodology. An application to real data from the Spanish household budget survey gives estimators of ratios of food household expenditures by provinces.

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Benchmarking a triplet of official estimates

Cited by 8 publications

References 7 publications

Using Small Area Estimation to Produce Official Statistics

Using Small Area Estimation to Produce Official Statistics

Smoothing County-Level Sampling Variances to Improve Small Area Models’ Outputs

Empirical best prediction of small area bivariate parameters

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