The role of the U.S. exchange‐rate equity market volatility on agricultural exports and forecasts
Bibliographic record
Abstract
Abstract This article estimates the U.S. state‐level soybean export forecast until December 2024 using a seasonal autoregressive integrated moving average (SARIMA) model. We utilize the newly developed exchange‐rate equity market volatility (EMV‐EX) to improve model fit and the Dirichlet process mixture model (DPMM) to control for unobserved heterogeneity. Using monthly data from January 2004 to December 2020, the study shows that soybean exports for states without ports are underestimated at the expense of states with ports. The EMV‐EX has a positive effect on soybean exports. The forecasts reveal no expected changes in the trends for soybean exports until December 2024. This study's results are useful to make and to implement more informed policy decisions for risk‐mitigating strategies such as the market‐facilitation program.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".