The limits of statistical information: How important are GDP revisions in Italy?
Bibliographic record
Abstract
The use of Gross Domestic Product (GDP) as a summary measure of the level of economic activity is pervasive in empirical economics, policy analysis and forecasting. This pervasive role of GDP and its nature of "public good" raises obvious problems of timeliness and accuracy of the data. A striking feature of GDP data (and, more generally, of all national accounts figures) is the presence of "data vintages". That is, the GDP estimate for a specific year or quarter is subject to several revisions after its first release. As a result, both the level and the profile of GDP over a given period may change, sometimes substantially, through time. This paper presents some evidence on the extent of GDP revisions in Italy, with particular emphasis on revisions of the quarterly national accounts series, and compares the Italian evidence with the available evidence from other countries. After discussing some areas in which data revisions can have potentially important consequences, the paper concludes with some policy recommendations.
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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 | 0.024 | 0.215 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".