Determinants of the nonperforming loans level movement in the banking sector of Serbia
Bibliographic record
Abstract
This paper offers an analysis of the regulatory and economic variables in the banking sector which may impact, to a greater or lesser extent, the level of nonperforming loans in Serbia. Although the banking sector, contrary to the rest of the economy, is recording positive results, there is also a simultaneously present and growing trend of nonperforming loans which is threatening to endanger the entire stability of the financial system. To that end, in the statistical and econometric analysis the point of departure were the following determinants: capital adequacy, the amount of loan loss provisions, profitability, ownership structure, and concentration in the banking sector, but also the growth rate of the real GDP. Using defined variables, what was examined was the stationary position of the observed series of data by means of an Augmented Dickey-Fuller (ADF) test of unit root, for the period from the last quarter of 2008 and up to the third quarter of 2013. According to the methodology of the National Bank of Serbia, quarterly data were used with the total of 20 observations. The main data sources were different statistical reports published by the National Bank of Serbia.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".