Comovement of oil prices with US economic indicators over the business cycle: facts and explanations
Bibliographic record
Abstract
Empirical industry-level studies find a systematic pattern of output and price responses to variations in oil prices. This pattern depends on the energy-intensity of production and on the origin of oil price shocks. We build a multisector business cycle model that features endogenous production of oil, multiple sources of oil price movements and intersectoral input-output linkages. The model explains the observed sectoral heterogeneity in output and price responses to oil prices changes, previously emphasized by empirical studies. In addition, we show that accounting for the sectoral linkages helps amplify the predicted effects of oil price changes at the aggregate level. / Plusieurs études empiriques ont trouvé une relation systématique entre les réactions du prix et de la production suite aux changements du prix du pétrole. Cette relation dépend de l’intensité énergétique de la production et de l’origine du choc du prix du pétrole. Nous construisons un modèle multisectoriel de cycle économique caractérisé par une production endogène du pétrole, plusieurs sources de variations de prix du pétrole, et par la présence de relations intersectorielles. Le modèle explique l’hétérogénéité observée dans les réponses de la production et des prix, suite aux variations du prix du pétrole, qui a été mentionnée dans les études empiriques. Nous montrons aussi que la prise en compte des relations interindustrielles permet d’amplifier les effets agrégés des changements du prix du pétrole.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".