Forecasting Inflation and the Inflation Risk Premiums Using Nominal Yields
Bibliographic record
Abstract
We provide a decomposition of nominal yields into real yields, expectations of future inflation and inflation risk premiums when real bonds or inflation swaps are unavailable or unreliable due to their relative illiquidity. We combine nominal yields with surveys of inflation forecasts within a no-arbitrage model where conditional expectations are latent but spanned by the history of the observed data, analog to a GARCH model for the conditional variance. The filtering problem is numerically trivial and we conduct a battery of out-of-sample comparisons. Our favored model matches the quarterly inflation forecasts from surveys and uses the information in yields to produce the best monthly forecasts. Moreover, we restrict the distribution of the inflation Sharpe ratios to achieve economically reasonable estimates of the inflation risk premium and of the real rates. We find that the inflation risk premium (i) is positive on average, (ii) rises when the unemployment rate increases and (iii) when the level of interest rates decreases. Hence, real yields are more pro-cyclical than nominal yields due to variations of the inflation risk premiums.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".