The macroeconomic effects of structural oil price shocks: An international GVAR analysis
Bibliographic record
Abstract
This paper investigates the macroeconomic impacts of structural oil price shocks by employing a Global Vector Autoregression (GVAR) framework, utilizing the structural shocks as identified by Baumeister and Hamilton (2019). Our analysis differentiates among three types of oil shocks: economic activity shocks caused by fluctuations in global demand, oil supply shocks driven by production disruptions, and oil inventory demand shocks linked to shifts in market expectations about future supply-demand imbalances. Empirical findings indicate that the macroeconomic consequences of these shocks differ depending on their underlying sources and related structural characteristics. In oil-importing countries such as Korea and China, oil supply disruptions and inventory-related shocks generally exert negative short-term effects on economic activity due to increased import costs and uncertainty-driven price volatility. Conversely, oil-exporting countries such as Canada and the United States respond differently, benefiting from increased export opportunities associated with higher oil prices. Overall, the study emphasizes the critical importance of distinguishing the structural causes of oil price fluctuations, highlighting how the indirect transmission of these shocks through international economic linkages significantly influences domestic macroeconomic performance outcomes. The results provide important implications for policymakers, underscoring the necessity of tailored policy responses to mitigate macroeconomic risks arising from energy transitions and geopolitical uncertainties.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".