The Shock Effect Study on the Impact of Financial Policies and Fiscal Expenditures on the Agricultural Products’ Prices in China
Bibliographic record
Abstract
According to the monthly data of agricultural products’ prices index from 1999 to 2012 and the related information about financial policies in China, the time series analysis methods has been made use and also I built a vector auto regression (VAR) model and vector error correction model (VECM) to finish the empirical analysis of impact factors on agricultural products .It is shown that money supply shock has a statistically significant impact on China’s agricultural products’ prices. The results indicate that: (a) In the long term, the growth of M0 will lead the fluctuations in the prices of agricultural products, while in the short term ,the supply of M2 and M1would play an important role. (b) In the long run, the prices of agricultural products in China have been subject to the level of fiscal expenditures and the exchange rate. But in the short term, the impact of the exchange rate is not significant. (c) By using Granger causality test method, the relationship between broad money supply (M2) and agricultural products’ prices is bidirectional. At last of the paper, some policy recommendations have been put forwards.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".