Dynamic Relationships between Macroeconomic Indicators and Non-Performing Loans in the Turkish Financial Industry
Bibliographic record
Abstract
We study the feasibility of predicting baking sector crises using macroeconomic stress tests via applications of the vector autoregressive approach on the dynamic relationship between Turkish banking sector's non-performing loans and the macroeconomic indicators; and find that during the period of 2004-2010 Turkish non performing loans ratio is primarily affected by previous non-performing loan levels, gross domestic product and imports. Our results show that for about eight quarters the impact of unexpected changes in the gross domestic product growth rate, the real exchange rate, and the import volume on the non performing loans is negative with the maximum reaction occurring in the third quarter. After eight quarters the impact of the shock subsides. On the other hand, unexpected changes in the nominal interest rate (the policy rate) lead to a negative reaction in the non performing loans for about five quarters, after which the reaction becomes positive. The results from this study imply that the strongest predictor of the banking sector non performing loans ratio is the previous values of the ratio itself, and that stress tests dependent solely on the macroeconomic indicators may not be sufficiently powerful in early prediction of future credit crises in Turkey.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".