Detecting Asymmetric Price Transmission with Consistent Threshold along the Fish Supply Chain
Bibliographic record
Abstract
The present study deals with asymmetric price transmission (APT) along the fish value chain by using a consistent threshold autoregressive (consistent TAR and momentum‐threshold autoregressive [M‐TAR]) model. A nonzero threshold captures strategic behaviors and adjustment costs that are not observable with small price changes around a zero threshold. Fish farming, because of greater control over supply, is expected to produce less asymmetry than wild harvesting. Asymmetry is notwithstanding found for both wild cod and farmed salmon marketed in France, but only with consistent thresholds and operating in opposite ways. The results are discussed with regard to the trade restrictions imposed by the Common Fisheries Policy. Cet article traite de l’asymétrie de transmission des prix (ATP) dans la filière des produits de la mer en utilisant un modèle autorégressif à effet de seuil (consistent TAR et M‐TAR). Un seuil non nul permet de révéler certains comportements stratégiques et des coûts fixes d’ajustement qui ne seraient pas observables sur de faibles variations de prix autour d’un seuil nul. On s’attend à ce que l’aquaculture, en vertu d’un degré de contrôle supérieur sur l’offre, engendre une plus faible asymétrie comparativement à la production halieutique. Une asymétrie de transmission est néanmoins observée à la fois dans le cas du cabillaud sauvage et du saumon d’élevage commercialisés en France, mais uniquement à partir de seuils endogènes et l’ATP agissant en sens opposé. Les résultats sont commentés au regard des restrictions commerciales imposés par la Politique Commune des Pêches européenne.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".