The Economic Consequence of Fed’s Monetary Policies in 2022: An Internal Perspectives
Bibliographic record
Abstract
US interest rate hikes due to COVID-19 and high inflation have affected the country's stock market and the international foreign exchange market. The authors analyze the willingness of U.S. interest rate hikes to cause inflation at the micro level through changes in the amount of money held by residents because of U.S. policies. This paper analyzes the impact of the U.S. interest rate hike by collecting various information on rental housing, GDP and spot exchange rates of countries affected by the U.S. According to the findings of this paper: 1. US interest rate hikes lead to a rise in the amount of currency held by citizens. 2. Interest rate hikes affect the GDP growth rate, and the stock market are positively correlated. 3. US monetary policy affects the world economy. The point of studying the Fed's rate hikes is to analyze the strengths and weaknesses of the policy and to find better ways to control inflation and make the economy stable. According to this paper, the authors propose that the United States should stop raising interest rates at the right time when the inflation rate falls to the right value and let interest rates return to their previous levels, otherwise it will stagnate the country's economic development and cause turmoil in the country's stock and foreign exchange markets.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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".