Return Predictability in the Treasury Market: Real Rates, Inflation, and Liquidity
Bibliographic record
Abstract
This chapter presents a joint empirical analysis of the sources of excess bond return predictability in nominal and inflation-indexed bonds in the United States and the United Kingdom. It discusses an empirically flexible approach to estimate the liquidity differential between inflation-indexed and nominal bond yields. This approach consists in regressing breakeven inflation onto bond market liquidity proxies while controlling for inflation expectation proxies. Liquidity proxies explain almost as much variation in U.S. breakeven as do inflation expectation proxies. Time-varying liquidity risk contributes statistically and economically significantly to predictability in inflation-indexed bond excess returns. The chapter uses a well-developed array of tools to address identification concerns in the presence of persistent variables, which can plague both ordinary least squares and affine term structure models. The estimated liquidity premium in U.S. Treasury Inflation-Protected Securities (TIPS) yields relative to nominal yields is economically significant and strongly time varying.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".