Determinant of net interest income of commercial banks in Indonesia
Bibliographic record
Abstract
This study aims to identify the factors that contribute to the formation of Net interest income (NII) for commercial banks in Indonesia in the short and long-term using the Vector Error Correction Model (VECM). The results showed that in the short term all variables in each period tend to adjust to achieve long-term balance. In the short term, the variables that affect NII are credit and NPL of large and retail trade, construction credit, transportation credit and NPL warehousing and communication, as well as lending rate facility. While in the variable length figures that affect NII are credit variables and NPL large and retail trade, Credit and NPL Transportation, warehousing and communication, other credit and Third-Party Funds (Deposit) collected. The analysis of Impulse Response Function can be proven that NII most quickly achieves stability when dealing with the shocks of large trade and retail NPL. Meanwhile, in the Forecasting Variance Decomposition analysis, it can be concluded that the variable that gives the greatest contribution to NII is the amount of construction credit.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".