The Impact of Crude Oil Price Changes in The Yield and Fluctuation of Manufacturing and Transportation Industries
Bibliographic record
Abstract
Given that Russia was one of the largest oil exporters, has left the oil market unstable as the war between Russia and Ukraine intensifies. This report, studies the effect of the oil shock on returns and volatility of manufacturing and transportation industries of the US, to understand the relationship, lag, and intensity between these industries in conjunction with the Crude oil price in the international market. By using Time-Series data collected from NYME and constructing a VAR model, an ARIMA-GARCH model has been formulated using likelihood ratio lag of 12. The report finds out no significant relationship between the oil shock triggered by Russian-Ukrainian war on the US transportation and manufacturing industry. The Yield and Volatility of these two industries have not been driven due to exogenous factors, crude oil price. There is a possibility that the impact is lagged and hasn’t occurred yet or is too statistically small that it is hard to be captured by the model. The impact of exogenous shocks may be lagged much higher until it destabilizes these sectors. Government must set countermeasures based on lag effect as the impact is indirect.
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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 | 0.000 | 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".