Asymmetric Exchange Rate Effects on Cross-Border Freight Flows between the United States and Canada
Bibliographic record
Abstract
This paper investigates possible asymmetric influences of the exchange rate on cross-border freight flows between the U.S.A. and Canada. Linear and nonlinear autoregressive distributed lag models are used to test for the existence of long-run asymmetric effects of 1) currency appreciation and depreciation and 2) exchange rate volatility changes on trade flows by truck, rail, air, vessel, and pipeline. This paper provides evidence that both currency value and exchange rate volatility affect the U.S.–Canada freight flows in an asymmetric manner. The long-run results of the nonlinear models show that exchange rate is found to be significantly associated with the bilateral trade flows between the U.S.A. and Canada. Exchange rate volatility tends to be significantly associated with trade flows in the nonlinear models, while its effects are insignificant in most cases in the linear models. These findings suggest that the conventional linear specification may mislead the asymmetric effects of exchange rate uncertainty on cross-border freight flows. It is also found that exchange rate sensitivities of U.S.–Canada trade flows by transport mode can differ significantly from those of aggregate trade flows. The information derived from disaggregate trade data can be useful for traders and shippers to develop a long-term strategic plan for infrastructure investment and service expansion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".