Bibliographic record
Abstract
This paper addresses the issues of Quarterly National Accounts compilation and Gross Domestic Product (GDP) revisions as well as GDP shortterm forecasting based on available monthly series of economic and financial indicators. If GDP is promptly and properly measured, policy makers and the general public can closely monitor implementation of the fiscal consolidation program. Reputation of the program depends on achievements that should be beyond any doubt. Since figures on quarterly GDP and its components are provisional until autumn of next year, and subject for revision over the next two years - which is a standard ESA 2010 methodology - accuracy of data might interfere with prompt availability. Additionally, nowcasting can provide timely estimates of current GDP. Figures on this quarter GDP are available two months after the end of the quarter. Flash estimates of GDP are available one month after the end of the quarter. The nowcasting technique can substantially shorten this gap. However, the challenging issue is related to a choice of the monthly series that should be included in Mixed Data Sampling econometrics. We address both of these issues in this paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".