A dynamic semi-nonparametric demand system: An application to U.S. pork import demand
Bibliographic record
Abstract
Price and substitution elasticities are important measures when conducting import demand analysis. However, measurement of these elasticitites is confounded by several factors. First, importing firms typically serve as the demanders of agricultural imports. Yet, agricultural import demand is often modeled as a final consumer demand. Second, use of flexible functional forms often results in violation of not just global, but also local regularity conditions of demand (either input or final good). This thesis addresses both of these issues using data on U.S pork imports from Canada, Denmark and the rest of the world. A dynamic Fourier cost function trade model based on producer theory is developed and a dynamic globally flexible AIDS model is also developed based on consumer theory. Dynamics are incorporated into the Fourier series expansion components in terms of habit persistent formation for the consumer model and the adjustment cots for the producer model. An empirical illustration of the difference in estimates between consumer model and producer model is provided. Estimates from the producer model satisfy the required regularity conditions, such as curvature condition of cost function and negative own price effects, whereas the consumer model does not satisfy the curvature condition. Results support the proposed dynamics and point to the proposed firm-based model as a potentially useful model in future applications. This study is the first empirical analysis of US pork import demand using the dynamic globally flexible function.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".