THE EFFECT OF MONETARY POLICY ON CREDIT SPREADS
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We analyze the effect of monetary policy on yield spreads between corporate bonds with different credit ratings over the business cycle. We use futures contracts to distinguish between expected and unexpected changes in the Fed funds target rate and several indicators to distinguish between different phases of the business cycle. In line with the predictions of imperfect capital market theories, we find that yields on corporate bonds with low credit ratings widen (narrow) with respect to those with high credit ratings following an unexpected increase (decrease) in the Fed funds target rate during recession periods. Several tests suggest that our results are robust to outliers, potential endogeneity problems, empirical specification, control variables, countercyclical risk premium in futures, and alternative definitions of credit spreads and economic conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it