Bibliographic record
Abstract
Abstract. Intertemporal models of the current account generally assume that global shocks do not affect the current account. We use this assumption to identify global and country‐specific shocks in a bivariate VAR of output and the current account. Cross‐country evidence from the G7 economies suggests that this identification works surprisingly well. We then employ our method to collect stylized facts on international macroeconomic fluctuations. We find that long‐term output growth is driven mainly by global factors in most G7 countries and that country‐specific shocks are less persistent and generally less volatile than global shocks. JEL Classification: F41, F43, C32 Fluctuations macroéconomiques internationales et compte courant. Les modèles inter‐temporels du compte courant postulent généralement que les chocs globaux n’affectent pas le compte courant. On utilise ce postulat pour identifier les chocs globaux et ceux qui sont spécifiques à des pays donnés dans un modèle VAR du produit global et du compte courant. Les résultats transversaux pour les pays du G7 suggèrent que cette forme d’identification donne de très bons résultats. On emploie cette méthode pour examiner des faits stylisés des fluctuations macro‐économiques internationales.Il appert que la croissance à long terme du produit dépend de facteurs globaux dans la plupart des pays du G7 et que les chocs particuliers aux pays ont un impact moins permanent et moins volatile que les chocs globaux.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".