The Near-Term Forward Yield Spread as a Leading Indicator: A Less Distorted Mirror
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 0.006 |
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