The Near-Term Forward Yield Spread as a Leading Indicator: A Less Distorted Mirror
Bibliographic record
Abstract
The spread between the yields on a 10-year US T-note and a 2-year T-note is commonly used as a harbinger of US recessions. We show that such “long-term spreads” are statistically dominated in forecasting models by an economically intuitive alternative, a “near-term forward spread.” This spread can be interpreted as a measure of market expectations for near-term conventional monetary policy rates. Its predictive power suggests that when market participants have expected—and priced in—a monetary policy easing over the subsequent year and a half, a recession was likely to follow. The near-term spread also has predicted four-quarter GDP growth with greater accuracy than survey consensus forecasts, and it has substantial predictive power for stock returns. Once a near-term spread is included in forecasting equations, yields on longer-term bonds maturing beyond six to eight quarters have no added value for forecasting recessions, GDP growth, or stock returns.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".