Effect of Macroeconomic Factors on Credit Risk of Banks in Developed and Developing Countries: Dynamic Panel Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Globalization phenomenon provided a suitable environment with new opportunities for investment in various countries. In this way, the issue of credit risk of countries and rankings of international ranking institutions has become more important. Owing to the fact that using values and numbers and quantization of the measured variables in evaluation of the risk of countries is considered as an appropriate tool for analysis of the economic status of each country. In this paper, it is tried to explore the economic conditions and the effect of macroeconomic variables on the credit risk of developed and developing countries. The model presented in this work can help managers of countries in economic and financial decisions of countries to prevent increase in credit risk and improvement of the credit. For this end, 14 countries in developed and developing countries were selected (developing: Iran, Brazil, Turkey, South Africa, china, Russia and India, developed countries: USA, GBK, Germany, France, Japan, Canada, Switzerland). Results of research revealed that credit risk of the past with regression coefficient as much as 1.174 has the highest contribution to the credit risk of the current period. Furthermore, results implied to the positive and significant effect of development on credit risk of countries.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it