Analyzing Asymmetry in Exchange Rates of Arctic Nations in Response to Oil Price Shocks
Bibliographic record
Abstract
ABSTRACT This study introduces a novel approach by utilising both structural VAR and nonlinear ARDL methods to investigate the short‐ and long‐term asymmetric effects of different oil shocks—oil supply, aggregate demand and oil‐specific demand shocks—on the exchange rates of six Arctic nations: Canada, Finland, Denmark, Norway, Russia and Sweden, over the period from January 1994 to December 2022. Our findings indicate that oil‐specific demand shocks have a substantial impact on exchange rates in both the short‐ and long‐term, while oil supply and aggregate demand shocks have minimal effects on currency fluctuations. Importantly, our analysis reveals new evidence of long‐term asymmetry in the influence of oil shocks on exchange rates in these Arctic economies, with asymmetric effects mainly manifesting over the long‐term. Thus, our research enriches our understanding of the distinct impacts of different oil price shocks on exchange rates, especially within the relatively unexplored context of Arctic economies.
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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 | 0.004 |
| 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 | 0.000 |
| 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".