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Andreea L. Erciulescu

National Institute of Statistical Sciences and USDA National Agricultural Statistics Service



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Nathan B. Cruze

USDA National Agricultural Statistics Service



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Balgobin Nandram

Department of Mathematical Sciences, Worcester Polytechnic Institute



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613 – Practical Applications of Small Area Estimation

Small Area Estimates for End-of-Season Agricultural Quantities

Sponsor: Survey Research Methods Section
Keywords: Auxiliary information, Bayes Estimates, Crop estimates, End-of Season Yield, Multiple Stage Benchmarking, Official Statistics

Andreea L. Erciulescu

National Institute of Statistical Sciences and USDA National Agricultural Statistics Service

Nathan B. Cruze

USDA National Agricultural Statistics Service

Balgobin Nandram

Department of Mathematical Sciences, Worcester Polytechnic Institute

The publication of official statistics at different levels of aggregation requires a benchmarking step. Difficulties arise when a benchmarking method needs to be applied to a triplet of related estimates, at multiple stages of aggregation. For ratios of totals, external benchmarking constraints for the triplet (numerator, denominator, ratio) are that the weighted sum of denominator/numerator/ratio estimates equals to a constant. The benchmarking weight applied to the ratio estimates is a function of the denominator estimates. For example, the United States Department of Agriculture's National Agricultural Statistics Service's county-level, end-of-season acreage, production and yield estimates need to aggregate to the corresponding agricultural statistics district-level estimates, that also need to aggregate to the corresponding prepublished, state-level values. Moreover, the definition of yield, as the ratio of production to harvested acreage, needs to hold at the county level, at the agricultural statistics district level and at the state level. We discuss different approaches of applying benchmarking constraints to a triplet (numerator, denominator, ratio), at multiple stages of aggregation, where estimators are constructed for two of the three quantities, the third being derived as a result. County-level and agricultural statistics district-level, end-of-season acreage, production and yield estimates are constructed and compared using the different methods. Results are illustrated for a subset of the sampled commodities and states, in the year 2014.

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