HO DO NATURAL GAS AND OIL PRICES AFFECT INDUSTRIAL PRODUCTION IN G 7 COUNTRICES DURING THE RUSSIAN-UKRAINIAN WAR : BASED ON PANEL NARDL APPROACH
Bibliographic record
Abstract
Paper applies days data from 2021:M1-2022:M6, in G7 Countrices, namely US, UK, Japan, Italy, France, Canada, Germany to examines the long-run, examines the asymmetrics impact of Natural Gas and Oil Prices on Industrial Production in Times of Russia-Ukraine war. We use the Panel Data Nardl approach by (Shin et al., 2014) and asymmetrical Granger Causility test by (Hatemi-j, 2012).The results of this study reveal that there is a non-linear connection among the variables in the long run. As the empirical results of the Panel-NARDL model estimation shows that the response of Industrial Production to positive oil shocks is greater than the negative shocks. Other result the response of Industrial Production to negative Natural Gas shocks is greater than the positive shocks. According to Hatemi-J (2012), there is a bi-directional causality running from positive shocks and negative shocks to the oil price and natural Gas price to Industrial Production
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".