Threshold Effects and Asymmetric Price Adjustments in U.S. Dairy Markets
Why this work is in the frame
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Bibliographic record
Abstract
Recent volatility in food prices and the growing disparity between prices at various stages of the marketing channel has generated much interest among agricultural producers, consumers, and policy makers. This study examines the effect of nonlinear threshold dynamics on asymmetric price transmission for three U.S. dairy products (butter, cheese, and fluid milk) using threshold error correction models. The empirical result suggests that price transmission of changes between producer and retail stages of the marketing chain is asymmetric for butter and fluid milk, but not for cheese prices. Also, this paper's findings indicate that conclusions about price asymmetries depend on the model specification assumptions made about symmetry and threshold effects. Thus, previous studies that assumed symmetric behavior and ignored threshold effects may be misleading. La récente volatilité des prix des produits alimentaires et la disparité croissante entre les prix observés aux divers échelons du réseau de distribution ont suscité l'attention des producteurs agricoles, des consommateurs et des décideurs. La présente étude examine l'effet d'une dynamique non linéaire avec seuil sur la transmission asymétrique des prix dans le cas de trois produits laitiers aux États‐Unis (le beurre, le fromage et le lait de consommation), à l'aide de modèles à correction d'erreur avec seuil. Les résultats empiriques montrent que la transmission des variations de prix entre les producteurs agricoles et les détaillants est asymétrique dans le cas du beurre et du lait de consommation, mais ne l'est pas dans le cas du fromage. D'après les résultats de la présente étude, les conclusions concernant l'asymétrie des prix dépendent des hypothèses posées sur la symétrie et les effets de seuil. Par conséquent, les études antérieures dans lesquelles on a supposé un comportement symétrique et ignoré les effets de seuil pourraient induire en erreur.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it