Black Sea Wheat Market Integration with the International Wheat Markets: Some Evidence from Co-integration Analysis
Bibliographic record
Abstract
In the last two years, Russia and Ukraine together exported an average of 29 million tons of wheat per year (USDA), and have become important players in the international wheat market. This paper examines the nature of the short- and long-run wheat price dynamics between Ukraine and Russia and other major wheat exporters - United States, European Union (EU), and Canada. For this purpose we use cointegration techniques (both the Johansen ML test and the Engel and Granger procedure) as well as the error correction model. The results suggest that Russian price series are cointegrated with those of the EU, but not with Canadian or U.S. wheat prices. Ukrainian prices series are found not to be cointegrated with other series. The estimated long-run price transmission elasticity between Russian and French (a representative country of the EU) wheat prices is equal to 1.07. We found the short-term relationship between Russia and EU also to be statistically significant. We show that after the price change occurs in the French price, Russian wheat price adjusts to the change within 6 months.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 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".