Bibliographic record
Abstract
Based on two dimensions of system risk, this paper studies the changes in the future inflation risk level, and uses the out-of-sample quantile R2 to further evaluate the predictive accuracy of different systemic risk indicators on inflation risk. Firstly, we compute two systemic risk indicators, MES and volatility, with data of Chinese financial institutions. And then we explore the amplification effect of these indicators on future inflation risk, under the framework of quantile regression. We find that systematic risk indicators have a strong predictive ability for the inflation level at various quantiles. MES indicator that reflects individual risk can better predict future deflation risk, while volatility index has a stronger ability to predict inflation risk. We also find that systemic risk indicators of different dimensions have different effects on inflation risk and deflation risk. In general, the MES index, which captures the individual risk of the organization, have a greater impact on the future inflation risk. While indicator that measures volatility in financial markets has more influence on the extreme lower tail of inflation rates. Finally, we predict the distribution of inflation in China from March 2020 to June 2021, and visually show the distribution trend of future inflation with forecast fan charts.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".