The effects of quantitative easing on U.S. inflation and output
Bibliographic record
Abstract
The present dissertation aims to provide insight on how Quantitative Easing Programs impacts both GDP and CPI, specifically, how the purchase of securities ranging from U.S. Treasury Securities to Mortgaged-backed Securities (i.e., MBS) impact said variables. It focuses on the post-crisis period between the second quarter of 2009 and the last quarter of 2019, using U.S. Federal Reserve data resources. It uses monthly data collected for six time-series, giving a total of 129 observations (N=129) for each. Using a Vector Auto-Regressive (i.e., VAR) approach, this dissertation concluded that Q.E. triggers a response in both Industrial Production and CPI, persistent for at least 15 lag periods. This response is stronger for Industrial Production, having a comparatively weak impact on CPI. The nature of this impact (i.e., positive or negative) could not be definitely inferred, as, across the 15-lag period, the behavior was mostly intermittent. However, for Industrial Production the sign of the response ended on a positive value for M.B.S and U.S. Treasury Securities maturing in 5 to 10 Years. For CPI all the U.S. Treasury securities and M.B.S ended on a positive value, with the exception of the U.S. Treasury Securities Maturing in 10 Years. Thus, the claim that Q.E. Programs do not trigger any type of response at either the Industrial Production or CPI level is safely rejected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".