Relationship Between Geopolitical Risk In Major Oil Producing Countries and Oil Price
Bibliographic record
Abstract
This study has applied Granger causality tests and dynamic ordinary least squares (DOLS) models to examine the relationship between geopolitical risk in major oil-producing countries and the crude oil price before and after the 2008 financial crisis. The granger causality tests show that the geopolitical risk of Saudi Arabia, Russia, the United States and China granger cause changes in crude oil prices. The DOLS models show that the series in the model are cointegrated. The coefficients for the geopolitical index of Canada, Russia and China are significant before the 2008 financial crisis sample period. However, the DOLS model shows that the coefficients for all geopolitical indexes are insignificant after the 2008 financial crisis sample period. The general public and investors generally precept major oil exporters like Russia and Saudi Arabia as the major players in the oil market. However, after the 2008 financial crisis, the discrepancy in the economic needs of the major oil-producing countries has reduced their ability to co-operate crude oil prices. This study also discovered that China plays a significant role in the oil market.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".