An Empirical Comparison of Methods for Benchmarking Seasonally Adjusted Series to Annual Totals
Bibliographic record
Abstract
For benchmarking monthly and quarterly series to annual series and to the Economic Census every five years, the U.S. Census Bureau uses an iterative, nonlinear method known as the Causey-Trager method. However, the Census Bureau’s X−12−ARIMA seasonal adjustment program uses a modified Denton procedure to benchmark the seasonally adjusted series to the annual totals of the unadjusted series. Some users have requested a different benchmark method in X−12−ARIMA. Statistics Canada has proposed several different benchmark methods, including a regression procedure, to replace the method for benchmarking in X−12−ARIMA. Using a sample of U.S. time series, this paper investigates the properties of the benchmarks from the current procedure in X−12−ARIMA, the new methods proposed by Statistics Canada, and the Causey-Trager method for benchmarking the seasonally adjusted series to the annual totals of the raw data. The objective of this study is two-fold: 1) to look at some of the properties of the various benchmarking procedures under consideration for benchmarking seasonally adjusted series, and 2) to look at possible settings for the regression procedure from Statistics Canada. There were very consistent results with smooth benchmark factors and small discrepancies in the month-to-month changes with both the Causey-Trager method and some settings of the Regression method. The Causey-Trager method gave results that were more consistent for every month though there is still a problem with the distortion of the month-to-month percent changes at the beginning and ending of the year. The Regression method gave results with smaller revisions when new data was added.
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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 | 0.070 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".