The role of uncertainty, sentiment and cross‐country interactions in G7 output dynamics
Bibliographic record
Abstract
Abstract Output fluctuations in the G7 are characterized using a VAR model of countries’ actual and expected outputs and uncertainty over these. New measures are developed to quantify the relative importance of economic prospects‐versus‐uncertainty, global‐versus‐national effects and fundamentals‐versus‐sentiment for countries’ persistent output movements. National and global contributions are found to be equally important across the G7 although considerable differences exist between countries. Uncertainty, and especially cross‐country uncertainty, is important in propagating the effects of shocks and generates around 20% of countries’ persistent output movements on average. Fundamentals dominate output movements although, with an 80:20 split, sentiment plays a non‐negligible role. Résumé Le rôle de l’incertitude, du sentiment, et des interactions entre pays dans la dynamique de production du G7. Les fluctuations du produit agrégé dans le G7 sont caractérisées par un modèle vectoriel autorégressif des produits observés et anticipés des pays, ainsi que de l’incertitude qui entoure ces mesures. De nouvelles mesures sont développées pour quantifier l’importance relative des perspectives‐versus‐l’incertitude, des effets globaux‐versus‐nationaux, et des fondamentaux‐versus‐croyances et sentiments dans l’explication des mouvements persistants dans la production des pays. Il appert que les contributions des dimensions globales et nationales sont également importantes à travers le G7, même si des différences considérables existent entre pays. L’incertitude, et particulièrement l’incertitude entre pays, est importante dans le processus de propagation des effets des chocs, et génère environ 20 % en moyenne des mouvements de production persistants des pays. Les fondamentaux dominent les mouvements de la production, mais avec un rapport 80–20, le sentiment joue un rôle non‐négligeable.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".