Asymmetric Effects of Exchange Rate Changes on British Bilateral Trade Balances
Bibliographic record
Abstract
This research presents first empirical time series evidence of the asymmetric impact of exchange rate changes on Britain’s trade balances with her 8 trading partners. Recent advances in time series and cointegration analysis have allowed for the estimation of the nonlinear effects of currency depreciations on countries’ trade balances. To this extent, we employ the nonlinear version of the Autoregressive Distributed Lag (ARDL) approach to cointegration and error correction methodologies to examine whether pound appreciations affect trade differently than do pound depreciations. We use monthly trade data which runs from 1998M1 to 2015M11 to capture more robustly the asymmetric impacts of exchange rate changes on trade balances. Econometric results from the non-ARDL procedures reveal that there exist long-run relationships in the case of UK-Canada, UK-Germany, UK-Italy, UK-Japan, UK-Korea, and UK-US trade balance models. Nevertheless, we did not find any long-run relationship in the case of UK-Spain and UK-Norway trade balance models. We also present empirical evidence for the existence of long-run asymmetries of exchange rates in the case of UK-Germany, UK-Italy, UK-Korea, and UK-Japan trade balance models. This paper also discusses policy implications of the empirical results as well as offering policy recommendations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".