The impact of oil price shocks on exchange rates: A non-linear smooth-transition approach
Bibliographic record
Abstract
This paper contributes to the literature on the effects of oil price changes for developed and emerging oil-exporting countries by looking at the effects on real exchange rates, which affect a country’s terms of trade and hence its competitiveness. In contrast to previously used Markov-switching models where the adjustment process is abrupt and in a way a “black box, ” we employ smooth transition models that specify the functional form of the adjustment process and are explicit about what variables drive the process. These types of smooth transition models have been successfully used to model the time series behaviour of exchange rate movements but have not yet been used to explain how exchange rates react to changes in oil prices, as far as we know. The paper considers logistic (asymmetric) and exponential (symmetric) smooth transition adjustments of real exchange rates for six major oil exporting countries in response to three different shocks affecting oil prices: an oil supply shock, an oil-market specific demand shock, and a global economic demand shock. Preliminary results for Canada only: We detect no statistically significant non-linearities, be they asymmetric or symmetric, for the effects of oil supply shocks and oil-market specific demand shocks on the exchange rate. Instead, the effects are linear in these cases. However, global aggregate demand shocks have non-linear effects on exchange rates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".