SAS Global Forum 2007 Posters Paper 146-2007 Benchmarking Sub-Annual Series to Annual Totals – From Concepts to SAS
Bibliographic record
Abstract
Situations that require benchmarking are very common in statistical agencies. Benchmarking is defined as an adjustment of the level of a sub-annual series using auxiliary annual benchmarks. The sub-annual series is modified so that the annual sums of the sub-annual series are equal to the corresponding benchmarks. This is done while preserving the movement in the sub-annual series as much as possible as well as considering that the benchmarks at the end of the series might not be available yet. This paper illustrates the benchmarking methodology developed at Statistics Canada. The presented method is a special case of the general regression-based benchmarking model proposed by Dagum and Cholette (2006). The paper also presents the innovative implementation of the methodology with a complete SAS procedure called PROC BENCHMARKING, developed at Statistics Canada for UNIX and Microsoft Windows operating systems, using SAS/TOOLKIT®. The procedure is presented through a custom add-in task for SAS Enterprise Guide and Microsoft Office, developed to provide a user-friendly interface and produce analytical tables and graphs based on the benchmarking results. The methodology presented is primarily used by economists and analysts, while the accompanying SAS procedure and custom task require basic knowledge of SAS and SAS Enterprise Guide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.226 | 0.085 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".