Effect of Macroeconomic Factors on Credit Risk of Banks in Developed and Developing Countries: Dynamic Panel Method
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. Keywords: credit risk, developed and developing countries, panel model JEL Classifications: G, G2, G21
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".