How Has the Euro Changed the Monetary Transmission?
Bibliographic record
Abstract
This paper characterizes the transmission mechanism of monetary shocks across countries of the euro area, documents how this mechanism has changed with the introduction of the euro, and explores some potential explanations.The factor-augmented VAR (FAVAR) framework used is sufficiently rich to jointly model the euro area dynamics while permitting the transmission of shocks to be different across countries.We find important heterogeneity across countries in the effect of monetary shocks before the launch of the euro.In particular, we find that German interest-rate shocks triggered stronger responses of interest rates and consumption in some countries such as Italy and Spain than in Germany itself.According to our estimates, the creation of the euro has contributed 1) to a greater homogeneity of the transmission mechanism across countries, and 2) to an overall reduction in the effects of monetary shocks.Using a structural open-economy model, we argue that the combination of a change in the policy reaction function --mainly toward a more aggressive response to inflation and output --and the elimination of an exchange-rate risk can explain the evolution of the monetary transmission mechanism observed empirically.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".