The term structure of interest rates in a noisy information model
Bibliographic record
Abstract
We study the term structure of interest rates in an endowment economy with noisy information and CRRA preferences. Exogenous prices and consumption consist of both temporary and permanent components, but the household observes only their aggregate values. We show that on average the term spread in this environment is positive and on a scale close to what we observe in the data, a fact that many existing macroeconomic models struggle to reproduce without very large coefficients of relative risk aversion. In our partial-information framework, uncertainty about the decomposition of the endowment and prices into their temporary and permanent components combined with a negative correlation in consumption growth explain why the slope of the yield curve is positive on average. We estimate our model using Bayesian methods and US data from 1961–2007 and find that the average interest rate spread is 0.85 %, compared with 0.98 % in the data. Further, we estimate a coefficient of relative risk aversion of only 4.86. Noisy information accounts for 44 % of the scale of the term premium, with the remainder principally explained by real activity and nominal factors playing only a small role.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".