Bibliographic record
Abstract
We estimate a bivariate GARCH-in-Mean VAR with a BEKK variance specification, to investigate whether oil price volatility affects real economic activity. We use the same data set of thirty seven, aggregate and disaggregate, industrial production indices used by Herrera et al. (2011) as a proxy for real output and a post-1973 data sample. We check the robustness of our results by using two proxies for the price of oil, the West Texas Intermediate (WTI) oil price and the Refiners’ Acquisition Cost (RAC) of crude oil, and by testing for both nominal and real effects. We find significant evidence of nonlinearities for both aggregate and disaggregate indices. Our research highlights the importance of nominal prices and extreme events such as the Great Recession in the transmission of nonlinearities. Our results show that nonlinear impacts of the price of oil on the aggregate economy vary according to time period even within the post-1974 data. Since 2000, oil price volatility is up markedly, but the number of industries it impacts is down when compared with the full sample. This is in keeping with what one would expect, based on trend improvements in GDP per unit of energy use. However, for those series, where oil price volatility is significant, the impact of oil volatility is substantially higher than in the full sample; this runs contrary to what one might expect from the observed GDP per unit of energy use improvements.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".