A Dynamic Analysis of Influencing Factors in Price Fluctuation of Live Pigs --- Based on Statistical Data in Sichuan Province, China
Bibliographic record
Abstract
Based on the weekly price data about supervision on the “early warning system of live pig production in Sichuan Province”, this article made a dynamic analysis in the research objects of live pig price, corn price, piglet price and pork retail price, including cointegration relationship test, Granger causality test and impulse response analysis so as to analyze the long term and short term conduction effects among different variables within the system of live pig system. It was discovered from the cointegration analysis that, the conintegration relationship existed within the live pig price system in Sichuan Province. In the long run, influence of piglet price on price fluctuation of live pig price was greater than that of corn price. The opposite is true to the short run. It was discovered through Granger test that, within a single production cycle (4 to 6 months), the piglet price, corn price and pork price affected the live pig price under Granger significance, while corn price and piglet price were exogenous to the system. It was discovered through the impulse response analysis that within a single production cycle, impact of the live pig price on price fluctuation of piglet manifested a “positive-negative-positive” response, mostly a “negative” response on price fluctuation of pork and mostly a “positive” response on price fluctuation of corn price. Finally, the authors put forward suggestions of decomposition of interest of the live pig industrial chain and escalation of value, etc.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".