Oil Price Pass-Through in the EMU. An empirical study of the role of energy for oil price pass-through to inflation and inflation differentials
Bibliographic record
Abstract
This paper examines the relationship between oil prices and its pass-through to inflation and inflation differentials in the European Monetary Union from the first quarter of 1999 to the last quarter of 2021. By using local projections to derive impulse response functions of an oil price shock, the pass-through to the inflation level is examined focusing on the role of energy-related transmission channels. The same transmission channels are examined for pass-through of oil price inflation to inflation differentials using a Pooled OLS regression. The aim of this paper is to contribute to earlier research by giving an updated view on how exposed the European Monetary Union is to changes in oil prices. Our estimates show that the EMU is not sensitive to oil price shocks pertaining to the inflation level. As the examined transmission channels show small effects of pass-through to the inflation level, where Energy Dependency accounts for the largest effect. Moreover, the findings from examining inflation differentials show a negative linkage between oil price inflation and inflation differentials. Yet, the Transport Share of HICP is found causing a small, yet amplifying effect on inflation differentials. The linkage of the other transmission channels cannot be established for inflation differentials.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 0.001 |
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".