Statistical Challenges in Combining Survey and Auxiliary Data to Produce Official Statistics

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

doi:10.2478/jos-2020-0004

Cited by 13 publications

(18 citation statements)

References 12 publications

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“…For future investigations, we recommend exploring even finer domains (for example, with geography defined by states, counties, or metropolitan areas) and other data sources to help refine the prediction space (see, e.g., Erciulescu et al, 2020). The predictions could also be improved if good quality auxiliary data could be obtained and harmonised with the NCS survey data, to construct additional covariates for the small domain models; any such data would have to be available and allowed to be used in the production environment of the agency.…”

Section: Discussionmentioning

confidence: 99%

A Model-Based Approach to Predict Employee Compensation Components

Erciulescu

Opsomer

2022

Journal of the Royal Statistical Society Series C: Applied Statistics

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The demand for official statistics at fine levels is motivating researchers to explore estimation methods that extend beyond the traditional survey‐based estimation. For this work, the challenge originated with the US Bureau of Labor Statistics, who conducts the National Compensation Survey to collect compensation data from a nationwide sample of establishments. The objective is to obtain predictions of the wage and non‐wage components of compensation for a large number of employment domains defined by detailed job characteristics. Survey estimates are only available for a small subset of these domains. To address the objective, we developed a bivariate hierarchical Bayes model that jointly predicts the wage and non‐wage compensation components for a large number of employment domains defined by detailed job characteristics. We also discuss solutions to some practical challenges encountered in implementing small area estimation methods in large‐scale settings, including methods for defining the prediction space, for constructing and selecting the information that serves as model input, and for obtaining stable survey variance and covariance estimates.

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Section: Discussionmentioning

confidence: 99%

A Model-Based Approach to Predict Employee Compensation Components

Erciulescu

Opsomer

2022

Journal of the Royal Statistical Society Series C: Applied Statistics

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“…In some cases, the survey may not indicate any planted area with respect to a particular commodity, but administrative data might represent some positive acres or vice versa. Erciulescu et al (2020) used the nearest neighbor methods to impute missing data for either survey or covariates. This approach of imputing and borrowing information from previous year or the average of several years estimates are being explored.…”

Section: Discussionmentioning

confidence: 99%

“…In this paper, models with constraints are considered based on Nandram et al (2022). For comparison, the model without constraints are from Erciulescu et al (2020).…”

Section: Modelsmentioning

confidence: 99%

Hierarchical Bayesian Model with Inequality Constraints for US County Estimates

Chen

Nandram

Cruze

2022

Journal of Official Statistics

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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.

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“…Stats 2022, 5 and can be applied to surveys suffering from small area issues, such as unavailable or unreliable survey summaries due to the volatility of sampling variances associated with the survey design. For ease of understanding, we illustrate the two methods with data from the United States Department of Agriculture's (USDA's) National Agricultural Statistics Service (NASS) crops county estimates program.…”

Section: Introductionmentioning

confidence: 99%

“…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

Stats

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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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Statistical Challenges in Combining Survey and Auxiliary Data to Produce Official Statistics

Cited by 13 publications

References 12 publications

A Model-Based Approach to Predict Employee Compensation Components

A Model-Based Approach to Predict Employee Compensation Components

Hierarchical Bayesian Model with Inequality Constraints for US County Estimates

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

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