Bibliographic record
Abstract
We apply two commonly used cointegration techniques to study the relation between corporate yields and government yields and derive implications for the relation between yield spreads and government yields. Due to the stationary nature of the yield spread data, based on results of the conventional unit root tests, we cannot use cointegration theory to test this directly. The results of the unrestricted impulse response analysis provide evidence, which contradicts the results of cointegration analysis applied to corporate and government yields. Our expectation of a positive long-run relation between yield spreads and government yields is only slightly realized for AA yield spreads. The effect of a shock to the 10-year government yield appears to have a consistently negative impact on A and BBB yield spreads, both over the short-run and the long-run. The negative yield spread- government rate relation is induced due to the over-representation of callable bonds in the sample of bond indices. Moreover, yield spreads appear to exhibit characteristics similar to long-memory processes, for which the order of integration lies between zero and one. The hypothesis of fractional integration has to be tested using a completely different set of statistical tools and is not examined in this paper.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".