Bibliographic record
Abstract
Significant fluctuations in crude oil prices draw attention from policymakers, academics, and practitioners. These fluctuations often arise from global demand changes, supply disruptions, or precautionary motives, prompting critical questions about monetary policy responses. Understanding the interplay between oil shocks and monetary policy requires examining central bank actions and their economic impacts. This study investigates monetary policy responses to oil shocks since the 1990s using Structural Vector Autoregression, Impulse Response Functions, and Variance Decomposition. These methods reveal dynamic relationships between crude oil prices, inflation rates, and monetary policy rates. The findings highlight distinct responses among countries. Major oil importers like the U.S. and China significantly raise policy rates in response to oil shocks, while Japan shows a more modest reaction. Among oil-exporting nations, Saudi Arabia and Canada respond swiftly and substantially, whereas Nigeria adopts an unconventional approach, loosening monetary policy after an oil shock. These variations underscore the complex interactions between oil prices and monetary policy globally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".