Bibliographic record
Abstract
In this paper, whether specific methods of conducting central bank interventions increase the likelihood of achieving its objectives in analysed. Daily Bundesbank and Fed intervention data covering the entire Post–Plaza period are used to estimate binary choice models over the sample of observations when at least one of the two central banks were intervening. The results suggest that central banks can, in fact, improve the likelihood of success primarily through coordination and that unilateral intervention conducted by the Bundesbank appears to have been destabilizing. Furthermore, it is shown that relatively infrequent intervention has a higher likelihood of success. JEL classification: E58, F31, F42, G15 Intervention dans le monde du taux de change dollar/mark après l’Accord de Plaza. Ce mémoire explore la question à savoir si des méthodes spécifiques d’intervention par la banque centrale accroissent la probabilité que les objectifs soient atteints. A l’aide de données sur l’impact des interventions de la Bundesbank et de la Federal Reserve pour toute la période qui a suivi l’Accord de Plaza, on calibre les modèles de choix binaires pour l’échantillon des données quand au moins l’une des deux banques centrales est intervenue. Les résultats suggèrent que les banques centrales peuvent en fait améliorer la probabilité de succès dans la poursuite des objectifs via la collaboration et que les interventions unilatérales de la Bundesbank semblent avoirété déstabilisantes. De plus, il semble que des inteerventions relativement moinsfréquentes ont une plus grande probabilité de succès.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| 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".