The Effect of Quantitative Easing on Investment in the US and UK: An SVAR Approach
Bibliographic record
Abstract
Following the collapse of Lehman Brothers in 2008, the Federal Reserve and the Bank of England implemented asset purchase programs to provide further liquidity to faltering markets, and to continue to place downward pressure on market interest rates. Later called Quantitative Easing, the higher asset prices and lower market yields induced by the purchases were expected to translate into lower market borrowing costs and increased investment. This project focused on estimating the effect of Quantitative Easing on real investment in the US and UK up to 2010. First, the historical relationship between bond yields and investment was estimated using a time series econometric model called a structural vector autoregression. Next, using the historical relationship between bond yields and investment, the impact of the asset purchases on investment was calculated using the bond yield changes induced by Quantitative Easing announcements. Deviations in bond yields on Quantitative Easing announcement dates suggested an impact on investment of 5.93% in the US, and an impact of 3.37% in the UK. Moreover, both the US and UK econometric results are statistically significant. Taking into account the econometric assumptions required to estimate the impact of Quantitative Easing on investment, the results in this project should be viewed with caution. However, the results will be useful in framing future thought on Quantitative Easing as a tool to provide macroeconomic stability
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".