A Dynamic Approach to Estimating and Testing Separability in U.S. Demand for Imported and Domestic Meats
Bibliographic record
Abstract
The paper develops dynamic model of demand for domestic and imported meats and compares the performance of the dynamic model called the general AIDS error correction model (GAECM) with the static linear approximate almost ideal demand system (LA/AIDS) model. A test for separability is developed based on the long‐run solution to the dynamic model. The results indicate rejection of the static model in favor of the dynamic model. Homogeneity and symmetry are shown to hold for the dynamic model but are strongly rejected for the static model. Finally, separability between domestic and imported meats is rejected for all models at the 5% level of significance. However, the results of the dynamic model based on the corrected likelihood test with a low p‐value (p= 0.038) indicate that rejection of separability is borderline. Le présent article porte sur l'élaboration d'un modèle dynamique de la demande de viandes produites au pays et de viandes importées, et compare la performance du modèle dynamique appelé modèle général de correction d'erreur AIDS avec le modèle statique LA/AIDS. Un test de séparabilité est élaboré d'après la solution à long terme du modèle dynamique. Les résultats indiquent le rejet du modèle statique en faveur du modèle dynamique. L'homogénéité et la symétrie sont valables pour le modèle dynamique, mais sont fortement rejetées dans le cas du modèle statique. Finalement, la séparabilité entre les viandes produites au pays et les viandes importées est rejetée pour tous les modèles à un seuil de signification de 5 p. 100. Cependant, les résultats du modèle dynamique, fondés sur la correction du test du rapport des vraisemblances avec une faible valeur p (p = 0,038), indiquent que le rejet de la séparabilité se situe à la limite.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".