Comparison of International Differences in the Volatility of Economic Growth and Non-Performing Loan Ratio: A Statistical Study Based on the Quantile Regression Model
Bibliographic record
Abstract
What is the level of non-performing loans in China’s banking sector and in different countries? Has the relationship between economic growth and the non-performing loan ratio changed? Is there a difference in the effect of the economic growth of different economies on the rate of non-performing loans in the banking sector? This study analyzes the relationship between economic growth and the non-performing loan ratios and characteristics of 13 countries from 2005-2014 based on quantile regression models with panel data. The results showed that the relationship between economic growth and the non-performing loan ratio was positive before the financial crisis in 2008 but was negative after 2008. The non-performing loan ratio in Canada, Mexico, and the US was low before 2008 and high after 2008. The impact of economic growth on the non-performing loan ratio was more significant for countries with a high non-performing loan ratio than for countries with a low non-performing loan ratio.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".