The Effects of Imports and Economic Growth in Chinese Economy: A Granger Causality Approach under VAR Framework
Bibliographic record
Abstract
This study inspects the association between economic growth and imports from China, based on data sourced from 2000 to 2021. For this reason, a quantitative research approach is used to determine the causality between the variables and their impact on the economy. The null hypothesis of the paper implies that the import growth rate has a significant impact on the GDP growth rate in the Peoples Republic of China. This hypothesis was rejected via the Granger causality test, as the only single directional relationship was found. However, further analysis was conducted by applying a Vector Auto-Regression (VAR) model that included leading macroeconomic variables, such as the inflation rate, the bank rate, and the exchange rate between the US dollar and Chinese yuan. The impulse responses of the model, aligned with the economic theory and the results, suggested that the import growth rate is negatively related to the GDP growth rate, while the GDP growth rate has an initial positive impact on the imports for the first three quarters, which later changes to a negative impact. This time lag suggests that while the impact between the variables is important, negative outcomes could be avoided if proper economic policy is implemented. The government of China should focus on policy implications that further promote export and substitute imported goods with domestic production.
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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) | 0.000 | 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".