Changes and Impact of Monetary Policy in the United States: Quantitative Easing and Federal rate hikes
Bibliographic record
Abstract
In response to the 2008 financial crisis, marked by the collapse of Wall Street giants like Bear Stearns and Lehman Brothers, the U.S. faced significant economic challenges. Traditional monetary policy tools proved ineffective as interest rates neared the Zero Lower Bound. Consequently, the Federal Reserve implemented Quantitative Easing (QE) as an unconventional measure to stimulate financial markets. This policy was executed in four phases, and by the end of the fourth round, U.S. inflation rates surpassed the 2022 target. Elevated inflation impacted living standards, prompting the FED to introduce interest rate hikes in 2022 to control it. Supply chain disruptions were a major contributor to this inflation spike. This paper uses qualitative and statistical methods to explore QE and the subsequent FED rate hikes. It emphasizes that QE essentially expanded the FED's Balance Sheet and uses visual data like graphs to illustrate the FED's actions since 2022. The article provides a comprehensive view of U.S. monetary policy adaptations during and after the financial crisis. The first three QE rounds addressed the 2008 crisis aftermath, while QE4 combated the economic fallout of COVID-19. The 4 rounds of QE and the last 2 years of Fed rate hikes have been generally successful. Both monetary policies have had some proven effect on the US economy. However, by critical thinking, QE has potential downsides, like fostering economic bubbles and inflation. And the Fed's repeated interest rate hikes have led to the problem of bank failure. Therefore, the Fed's monetary policy choices, especially unconventional ones like QE, require weighing benefits against potential risks.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".