To What Degree and through Which Channel Do Central Banks Other Than the Federal Reserve Cause Spillovers?
Bibliographic record
Abstract
Spillovers play a crucial role in driving monetary policy around the world.The literature focuses predominantly on spillovers from the Federal Reserve.Less attention has been paid to spillovers from other central banks.I measure the degree to which 20 central banks cause spillovers.I show that central banks in medium-to high-income countries cause spillovers to medium-to longterm interest rates in similar countries through a bond-pricing channel.These effects are narrower than spillovers from the Federal Reserve, which also affect emerging markets, shortterm interest rates, and other assets.However, they are still pronounced.Fourteen central banks other than the Federal Reserve cause significant spillovers: the central banks of Australia, Canada, Czechia, the eurozone, Japan, Mexico, Norway, New Zealand, Poland, Romania, South Korea, Sweden, Switzerland, and the United Kingdom.Consequently, the Federal Reserve causes only one-fifth of the spillovers to 10-year interest rates, and the United States is the recipient of large spillovers.My results imply that central banks, especially the Federal Reserve, are affected by greater spillovers than is commonly believed, and that non-Fed central banks cause spillovers through a bond-pricing channel.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".