Assessing the Exchange Rate Volatility as an External Shock to Chinese Economy
Bibliographic record
Abstract
Theoretically, the rate of exchange is one of the major drivers of inflation that influences the wholesale price index (WPI) in countries where significant emphasis is put over import and export like China. In this paper, the exchange rate’s clustering volatility and its impulsiveness as an external shock to WPI is investigated on a set of time series data which represents 4,067 daily observation of Chinese Economy from August 12, 2004-September 30, 2015. The ordinary least square and weighted regression analysis reveal significant p-values of 0.000 for exchange rate that explain the WPI throughout the stated period. The autoregressive conditional heteroskedasticity and generalized autoregressive conditional heteroskedasticity model exhibit significant probability value of 0.000 and 0.044 respectively for WPI and the exchange rate. It is found that the previous days’ volatility of WPI influences the future volatility of WPI as an internal shock in addition to the previous days’ impulsiveness of the exchange rate which influences the future volatility of the WPI as an external shock. The testing models are thoroughly applied and their stability and validity are evidenced thereto.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".