An evaluation of the professional forecasts of U.S. long‐term interest rates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper evaluates the multiperiod forecasts of Moody's Aaa corporate and the 10‐year Treasury bond rates from the Survey of Professional Forecasters (SPF). We show that the SPF forecasts are not rational since they fail to be unbiased and, in some cases, do not fully incorporate the information in the past actual rates. These forecasts, however, are useful, since they are able to accurately predict the direction of change in the actual series. We also formulate a model that utilizes the information in the SPF forecasts of the unemployment rate. Comparable four‐quarter‐ahead forecasts of the two interest rates from this model are shown to be significantly more accurate than the corresponding SPF forecasts for 2001.1–2004.4.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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