Macro Risks and the Term Structure of Interest Rates
Bibliographic record
Abstract
We extract aggregate supply and aggregate demand shocks for the US economy from macroeconomic data on inflation, real GDP growth, core inflation and the unemployment gap.We first use unconditional non-Gaussian features in the data to achieve identification of these structural shocks while imposing minimal economic assumptions.We find that recessions in the 1970s and 1980s are better characterized as driven by supply shocks while later recessions were driven primarily by demand shocks.The Great Recession exhibited large negative shocks to both demand and supply.We then use conditional (time-varying) non-Gaussian features of the structural shocks to estimate "macro risk factors" for supply and demand shocks that drive "bad" (negatively skewed) and "good" (positively skewed) variation for supply and demand shocks.The Great Moderation, a general decline in the volatility of many macroeconomic time series since the 1980s, is mostly accounted for by a reduction in the good demand variance risk factor.In contrast, the risk factors driving bad variance for both supply and demand shocks, which account for most recessions, show no secular decline.Finally, we find that macro risks significantly contribute to the variation in yields, bond risk premiums and the term premium.While overall bond risk premiums are counter-cyclical, an increase in bad demand variance is associated with lower risk premiums on bonds.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".