Have Agricultural Derivatives and International Trade Boost the Brazilian Agricultural Sector? Empirical Evidence via Long-Run Non-Causality Test
Bibliographic record
Abstract
This work analyzes long-run Granger causality relationships between Brazilian agricultural production, agricultural derivatives from the Brazilian Stock Exchange, international trade, interest rates, and nominal exchange rates. The methodology used is the long-run block Granger non-causality test proposed by Yamamoto and Kurozumi (2006) within a Vector Error Correction Model (VEC), for the period of 2001 to 2020. The generalized inverse procedure was applied to test for long-run Granger causality among the variables, with the degeneracy of the covariance matrix of the estimator detected through the test developed by Kurozumi (2003). The key findings confirm a long-run causal relationship from international trade to interest rates and agricultural derivatives, as well as from the latter two to the block consisting of international trade and agricultural output. The robustness analysis conducted validated the influence of derivatives on the economic growth of the agricultural sector over the past two decades.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".