Dutch GDP Data Revisions: Are They Predictable and Where Do They Come from?
Bibliographic record
Abstract
This paper examines whether the preliminary releases of GDP incorporate efficiently all available information or whether the preliminary estimates contain information that can be useful in predicting forthcoming GDP data revisions. Forecast rationality tests are applied to distinguish between these two characterisations. We analyse the revision over three horizons: the very short term revision after one quarter, the short-term revision after two years and the long-term revision. We find evidence of predictability for all short- and long-term revisions of Dutch GDP data. Our evidence for the revisions of the seasonally adjusted quarter-on-quarter growth rates are in line with the findings of G7 countries. Moreover, we analyse the revisions of the six expenditure components and ten prodcution components that constitute GDP. Only the preliminary releases of household consumption and the construction sector seem to explain the GDP data revisions. However, the general conclusion is that the forecast rationality hypothesis is rejected for almost all components separately, while almost no individual component's preliminary data release can forecast the revision of GDP.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".