Rediscounting Under Aggregate Risk With Moral Hazard.” Federal Reserve Bank of New York Staff Report No. 296 and Bank of Canada Working Paper 2007-XX
Bibliographic record
Abstract
Freeman (1999) proposes a model in which discount window lending and open market operations have different effects. This is important because in most of the literature, these policies are indistinguishable. However, Freeman’s argument, that the central bank should absorb losses associated with default to provide risk-sharing, stands in stark contrast with the concern that central banks should limit their exposure to credit risk. We extend Freeman’s model by introducing moral hazard. With moral hazard, the central bank should avoid absorbing losses and Free-man’s argument breaks down. However, we show that policies resembling discount window lending and open market operations can still be distin-guished in this new framework. The optimal policy is for the central bank to make a restricted number of creditors compete for funds. By restricting the number of agents, the central bank can limit the moral hazard problem. By making them compete with each other, the cen-tral bank can exploit market information that reveals the state of the economy.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".