Assessing the Exchange Rate Sensitivity of U.S. Bilateral Agricultural Trade
Bibliographic record
Abstract
This paper uses an autoregressive distributed lag approach to cointegration to examine the short‐ and long‐run effects of exchange rate changes on bilateral trade of agricultural products between the United States and its 10 major trading partners. Results show that, in the long run, while U.S. agricultural exports are highly sensitive to bilateral exchange rates and foreign income, U.S. agricultural imports are mostly responsive to the U.S. domestic income. In the short run, on the other hand, both the bilateral exchange rates and income in the United States and its trading partners are found to have significant impacts on U.S. agricultural exports and imports. Dans le présent article, nous avons utilisé un modèle autorégressif à retards échelonnés (autoregressive distributed lag (ARDL) approach to cointegration) pour examiner les effets à court et à long terme des variations de taux de change sur le commerce bilatéral des produits agricoles entre les États‐Unis et ses dix principaux partenaires commerciaux. Les résultats ont montré que, à long terme, bien que les exportations agricoles des États‐Unis soient très sensibles aux taux de change bilatéraux et au revenu étranger, les importations agricoles des États‐Unis sont principalement sensibles au revenu intérieur des États‐Unis. À court terme, par contre, les taux de change bilatéraux et le revenu des États‐Unis et de ses partenaires commerciaux ont des répercussions considérables sur les exportations et les importations agricoles des États‐Unis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".