Assessing The Impact of Oil Prices on Economic Growth in Oil Importing and Oil Exporting Countries
Bibliographic record
Abstract
This study investigates the impact of oil prices on economic growth in oil importing and oil exporting countries covering the period from 1995 to 2021. Overall, there are 10 oil importing (China, Germany, Italy, India, Japan, Netherlands, South Korea, Spain and Thailand) and 10 oil exporting (Brazil, Canada, Iran, Kuwait, Mexico, Nigeria, Norway, Russia, Saudi Arabia and United Arab Emirates) countries have been analysed in this study. Moving on, the study employed panel unit root test, panel cointegration test, FMOLS, panel ARDL estimation and panel Granger causality test to analyzed the variables. The findings of the study shows that oil price has a positive relationship with economic growth in oil exporting nations in both short run and long-run. However, oil price has a negative relationship on economic growth in oil importing nations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".