Analisis Kredit UMKM di Provinsi Aceh: Analisis Empiris Vector Error Correction Model (VECM)
Bibliographic record
Abstract
The purpose of this study was to determine the effect of regional gross domestic product, non-performing loans, and loan interest rates on credit absorption by SMEs in Aceh province in the long term. The data used is secondary data in the form of a quarter 1st quarter 1995 to third quarter 2015. The model used in this study is a model of Vector Error Correction Model (VECM) to find out the results of short-term estimates, and using Johansen cointegration test to determine the relationship long-term between variables. The data used in this study has been tested with Augmented Dickey Fuller (ADF) to determine the stationary data. Based on this study it was found that in the long term there is a cointegration relationship between the variables studied. In the short term, the variables affecting the gross regional domestic product and has a one-way relationship with SME loans while variable interest rates have a causal relationship with SME loans in Aceh province, while the NPL variable does not have a causal relationship with SME loans. Keywords: SME Loans, Gross Domestic Product, Non Performimg Loan, Interest Rates, Vector Error Correction Model (VECM).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".