Unconventional Monetary Policy in the United States : An empirical study of the quantitative easing (QE) effects on households and firms
Bibliographic record
Abstract
Quantitative Easing is an unconventional instrument when conducting monetary policy with the aim of stimulating the economy. The instrument is a complementary tool when changing the nominal interest rate is no longer effective. In the United States this unconventional instrument has been used through three different waves between December 2008 to October 2014. This research paper investigates two different regressions, one for the dependent variable consumption and one for the dependent variable investments to capture the effects on households and firms respectively. The results are used to study whether the unconventional monetary policy has had any effects on these variables and if the dependent variables are affected to different degrees. Data for this paper is collected between the first quarter of 2005 until the fourth quarter of 2019. The modelling used is the Auto Regressive Distributed Lag Model (ARDL) for the two different regressions. All variables in the regressions are critically tested for unit roots, autocorrelation, heteroscedasticity and misspecification to validate the analysis. The findings of our ARDL models indicate that investments are affected by quantitative easing to a larger degree than consumption by 3.8 times the change of the coefficients at its optimal lags.
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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 | 0.000 |
| 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".