Dynamic Interrelationships in Hard Wheat Basis Markets
Bibliographic record
Abstract
Basis values for hard red spring (HRS) wheat have escalated radically, experienced extraordinary levels of volatility (risk), were subject to a squeeze during 2008, and all these have important implications for market participants. These are particularly important to marketers in the Northern Great Plains in the United States, as well as for Canadian marketers as they confront deregulation in wheat marketing and will be exposed to these risks. The purpose of this paper is to analyze the dynamic relationships and interdependencies among terminal market basis values for milling. Specifically, we seek to identify factors impacting basis values for 13%, 14%, and 15% protein HRS wheat in addition to the intermarket wheat spread between Minneapolis and Kansas City wheat futures. We specify a vector autoregression (VAR) model to explore these relationships. Exogenous structural variables are specified in addition to dynamic interrelationships including seasonal and intertemporal variability and dynamic interdependencies among these markets and relationships. Results of interest are that: (1) basis values for these markets have been trending up and have become more volatile; (2) factors impacting this variability are the protein level in HRS, production of hard red winter (HRW), and Canadian wheat (on high protein basis); (3) HRW protein supplies are not significant in the basis equations, but, do impact the intermarket wheat futures spread; (4) quality factors have a significant impact on basis values, notably vomitoxin, falling numbers, and absorption. Dynamic interrelations are also important in that all prices converge quickly toward a long‐term equilibrium. In addition, there are seasonal impacts, dynamic basis interactions, trends, and lagged impacts of protein levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".