Perbankan Umum Syariah Jangka Panjang Dan Pendek Terhadap Pertumbuhan Ekonomi Di Indonesia (Error Correction Model)
Bibliographic record
Abstract
Abstract: Purpose: This study aims to analyze the influence of Islamic banking which is reflected in: assets, financing, and third party funds of Islamic banking on economic growth in Indonesia. The data used in this study is time series data in the form of quarter period 2011:Q1-2020:Q4. Research methodology: This study uses regression analysis methods OLS (Ordinary Least Square) and ECM (Error Correction Model). The data used is time series data in the form of quarterly period 2011:Q1-2020:Q4. Results: The results of this study indicate that the asset variable in Islamic banking has a positive and significant effect on economic growth in Indonesia in the short and long term. The financing variable in Islamic banking has a positive and significant effect on economic growth in Indonesia in the short and long term. Likewise, the DPK (Third Party Funds) variable for Islamic banking has a positive and significant impact on economic growth in Indonesia, both in the short term and in the long term.Limitations: The limitation of this research is that there are many variables outside the model that are not included in the study. Contribution: The positive performance of the financial sector will have a positive correlation with the economic performance of a country. The financial sector can be the main source of growth in the real sector of the economy. Keywords: 1. Sharia Banking 2. Economic Growth 3. ECM (Error Correction Model)
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".