Short- and long-run relationships between Ukrainian barley and world feed grain export prices
Bibliographic record
Abstract
Over the past decade, Ukraine has become an important player in the international feed grain market. From 2004/05 to 2012/13 it exported on average 25 percent of the total world barley annually, less than a percent lower than the largest barley exporter in the world - Australia. This research summarizes the short- and long-run barley price dynamics between Ukraine, and other major barley exporters - Australia, European Union (EU), and Canada – from 2004 to 2010. We also include U.S. corn prices to check if there is any long-run relationship between these two feed grain prices. Tests of market price cointegration (Johansen ML test and residual-based tests) and threshold error correction techniques were performed for this purpose. The results suggest that the cointegrated pairs of prices are Ukraine-Australia, Ukraine-France, Australia-Canada, and Australia-France. The estimated long-run barley price transmission elasticity is 0.71 between Ukrainian and French (a representative country of the EU) barley prices, 0.59 between Australian and Ukrainian barley prices, 0.54 between Canadian and Australian barley prices, and 0.57 between Australian and Canadian barley prices. We also found the short-term relationships between the cointegrated prices to be statistically significant. Moreover, Ukrainian barley prices were found to be weakly exogenous with regards to the Australian and French barley prices in the analyzed period, while Australian barley price is weakly exogenous with regards to the French barley price. Price adjustments in all cointegrated price series were found to be symmetric.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".