Decomposed Oil-Driven Inflation Persistence and Asymmetric Shocks
Bibliographic record
Abstract
Abstract This paper proposes a framework for measuring inflation persistence using error- and intrinsic-based measures. We decompose the oil price into returns, cyclical components, permanent trends and innovations to estimate their distinct impacts on inflation persistence. The results reveal that oil returns and cyclical oil price components reduce inflation persistence in net-exporting oil economies but significantly increase it in net-importing oil economies. The permanent oil prices reduce the intrinsic-based persistence in the US, Germany, Canada, India, Brazil and Korea. Applying a regime-switching local projection, we identify asymmetric oil price impact on inflation persistence through the transmission channel of extreme oil price shock, exchange rate movements and intensity of geopolitical risk. Notably, in extreme geopolitical risks, inflation persistence significantly decreases in reaction to oil shocks in net-importing oil economies. These results show the need to address specific oil price components to manage inflation effectively and support central banks with more effective monetary policy measures that address oil-driven inflation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".