Should Monetary Policy Be Used to Counteract Financial Imbalances
Bibliographic record
Abstract
The authors examine whether monetary policy should and could do more to lean against financial imbalances (such as those associated with asset-price bubbles or unsustainable credit expansion) as they are building up, or whether its role should be limited to cleaning up the economic consequences as the imbalances unwind. Effective supervision and regulation are the first line of defence against financial imbalances. An important question is whether they should be the only one. The authors argue that the case for monetary policy to lean against financial imbalances depends on the sources of the shock or market failure and on the nature of the other regulatory instruments available. To the extent that financial imbalances are specific to a sector or market and that a well-targeted prudential tool is available, monetary policy might play a minor role in leaning against the imbalances. However, if the imbalances in a specific market can spill over to the entire economy and/or if the prudential tool is broad based, monetary policy is more likely to have a role to play. In such a case, there may be a need to coordinate the use of the two policy instruments.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".