An Empirical Study of Container Transportation Price Volatility under Uncertainty Shocks Based on DCC-GARCH and SVAR Models
Bibliographic record
Abstract
As a key link in international trade, the price volatility of container transportation has a profound impact on the global supply chain, and uncertainty shocks are one of the main causes of price volatility.With this topic, this paper measures the level of uncertainty at the policy level through the uncertainty index construction method, which lays the foundation for subsequent research.Dynamic correlation and impulse effect analyses of container transportation market prices under uncertainty shocks are conducted using DCC-GARCH and SVAR models.China's economic policy uncertainty index showed four stages of significant increase in 2001, 2008, 2015 and 2019.The overall price volatility of the container transportation market shows an upward trend, and in 2023, the transportation price is 23,835 yuan.Container transportation prices are affected by the uncertainty of China's economic policies as well as China's trade policies, with correlation coefficients ranging from -0.69 to 0.60.The influence of China's economic policy uncertainty index on container transportation price does not have a long time lag effect.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".