A Test of China' s Commercial Banks under the Falling Housing Price Pressure Based on CGE Model
Bibliographic record
Abstract
There exist apparent bubbles in Chinese housing markets at present. In order to prevent financial risks, it is truly necessary to squeeze some the bubbles by means of policy and market mechanism.From the perspective of general equilibrium, based on CGE model, this paper adopts the quarterly data from the first quarter of 2004 to the fourth quarter of 2013 together with the Wilson model to conduct a pressure test of the credit risks brought by China' s falling housing prices on the commercial bank system.The results indicate that if the falling housing prices exceed 30 percent, the commercial banks will suffer huge financial risks, even financial crisis may be triggered. The important implication for policy-making is that in order to prevent financial risks and avoid financial crisis, China' s macroscopic readjustment and control should not only inhibit the housing prices from rising excessively, but also avoid sharp dropping of housing prices.
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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".