Assessing the Impact of Demand Shocks on the US Term Premium
Bibliographic record
Abstract
During and after the Great Recession of 2008–09, conventional monetary policy in the United States and many other advanced economies was constrained by the effective lower bound (ELB) on nominal interest rates. Several central banks implemented large-scale asset purchase (LSAP) programs, more commonly known as quantitative easing or QE, to provide additional monetary stimulus. Gauging the effectiveness of LSAPs is important, since the ELB may be a constraint on conventional monetary policy more frequently in the future than it was in the past. In this paper we analyze two distinct periods where we observe exogenous demand shocks for 10-year US Treasury bonds to assess their impact on the term premium. Our results show that official sector demand factors, measured by purchases of securities by the foreign official sector and the Federal Reserve’s asset purchase program, are important drivers explaining movements in the term premium. They suggest that asset purchases (QE) can help provide additional monetary stimulus even once the policy rate has reached its ELB. Robustness tests also suggest that the estimated impact of official sector demand factors is the most robust driver of the term premium across alternative specifications, while the estimates on risk factors appear more sensitive to the choice of term premium specification. Based on external projections and authors’ assumptions, our results suggest that the US term premium will rise gradually from an average of about -20 basis points in the fourth quarter of 2016 to around +10, 32 and 60 basis points by the end of 2017, 2018 and 2019, respectively, before stabilizing around 100 basis points in the medium term.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".