Bibliographic record
Abstract
In this paper we focus on measuring the statistical properties of the revision process of quarterly national accounts released by Istat, with particular reference to quarter on quarter changes of real GDP and main components, measured in chained values (or at constant prices for old vintages) and net of calendar and seasonal effects, over the 15 years between 1999 Q1 and 2013 Q4. The performance of statistical indicators usually adopted to perform the revision analysis proves dependent on the time span specifically considered. Indeed our findings concerning both the last 15 years as a whole and the single quarters show uneven behaviour inside the year in the different quarter. The evidence based on developments over the full period shows that the average revisions are in general negligible; on the contrary, they turn statistically significant based on moving window analysis, that find some bias in the revisions between E2 and E1 concerning Gross fixed capital formation, Exports and Public consumption. Finally, the standard ‘news or noise’ analysis is performed in order to assess the ability of the preliminary estimates of GDP and its main components to efficiently forecast of the respective final estimate.
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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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".