Asymmetric Effects of Domestic and Foreign Economic Policy Uncertainty on Exchange Rates in Developed Countries
Bibliographic record
Abstract
We employ linear and nonlinear ARDL models using monthly data from January 1985 to April 2025 to examine whether domestic and foreign economic policy uncertainty (EPU) exert asymmetric effects on exchange rates. The analysis focuses on five major economies relative to the United States: Australia, Canada, Japan, the United Kingdom, and the Euro Area. The baseline linear ARDL model, excluding EPU variables, fails to establish a stable long-run relationship between exchange rates and economic fundamentals. In contrast, an extended linear ARDL model with domestic and foreign EPU identifies stable long-run relationships across all countries. The nonlinear ARDL (NARDL) model confirms long-run relationships and reveals significant short- and long-run asymmetries in EPU shock effects. Our findings demonstrate the importance of including domestic and foreign EPUs in exchange rate models, and the benefits of using nonlinear models to capture asymmetries between exchange rates and economic fundamentals. The Global Financial Crisis (GFC) reinforced the long-term relevance of EPU in exchange rate determination, while the COVID-19 pandemic introduced heightened short-run volatility and modest long-run structural adjustments, particularly in countries more vulnerable to external shocks.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".