The Asymmetric Effects of Oil Price Shocks on the Canadian Economy
Bibliographic record
Abstract
A threshold vector autoregression (TVAR) is estimated to study the effects of oil price shocks on Canadian output and price level. While much of the literature has investigated potential asymmetric effects of positive and negative oil price shocks within a linear vector autoregression (VAR), we do so within a nonlinear VAR. Further, we extend the analysis to consider the correlation between asymmetries associated with the business cycle phase and size/sign asymmetries. Positive oil price shocks are found to have a stronger effect on output than negative oil price shocks. This asymmetry is significant in recessions, but lessened during expansions. The results also suggest that the reduction in inflation due to a negative oil price shock is larger than the increase in inflation following a positive oil price shock, especially during periods of low output growth. Yet, neither inflation nor output growth seems to vary disproportionately with the size of the oil price shock. In general, the results are robust to the ordering of the variables in the VAR process and to the time window over which the net oil price change is computed
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".