Identifying Macroeconomic Linkages to U.S. Agricultural Trade Balance
Bibliographic record
Abstract
This study explores the short‐run and long‐run relationships between the U.S. agricultural trade balance and domestic macroeconomic aggregates and agricultural variables. We use cointegration analysis and a vector error‐correction model with quarterly data for 1981–2003. The results show that, in the long run, the exchange rate, agricultural price, and disposable income are weakly exogenous in the U.S. agricultural sector and have significant effects on the trade balance. The combined short‐run dynamic effects of the exchange rate, agricultural price and production, and the disposable income jointly explain changes in the trade balance. La présente étude porte sur les liens à court et à long terme entre la balance commerciale agricole des États‐Unis, les agrégats macroéconomiques et les variables agricoles. Nous avons utilisé une analyse de cointégration et un modèle vectoriel à correction d'erreur comprenant des données trimestrielles de 1981 à 2003. Les résultats ont montré que, à long terme, le taux de change, les prix agricoles et le revenu disponible sont faiblement exogènes dans le secteur agricole des États‐Unis et qu'ils ont des effets substantiels sur la balance commerciale. Les effets dynamiques à court terme combinés du taux de change, des prix et de la production agricoles ainsi que du revenu disponible expliquent les changements observés dans la balance commerciale.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".