1 REVISIONS TO QUARTERLY GDP ESTIMATES A COMPARATIVE ANALYSIS FOR SEVEN LARGE OECD COUNTRIES
Bibliographic record
Abstract
This paper examines the revisions histories of seven large OECD economies. Specifically it analyses the size of revisions to 1996-2000 constant price quarter-on-quarter GDP growth rates, comparing the size and direction of these revisions with earlier OECD studies, and concludes that the reliability of first (preliminary) estimates has improved, or at least, not deteriorated, in most countries. It shows that in most countries mean revisions for this data period have been of similar magnitude in all countries, with the exception of Japan, where they have tended to be larger but where recently implemented changes to compilation systems are expected to lead to future improvements. The paper also investigates whether preliminary estimates are systematically lower or higher than later estimates for the period in question. It finds some evidence of this in Canada, France and the UK but it is beyond the scope of this study to determine whether this reflects a systematic bias in data sources and compilation methods rather than one-off revisions such as changes in concepts. Moreover, the size of the sample used in this study is relatively small (20 observations). The paper cautions against the use of bias adjustments in estimating current and future growth rates. Rather, it advocates the use of more comprehensive investigations of the causes of bias so that these can be corrected at source. Revisions analysis databases are important tools in assessing and
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".