Efektivitas Kebijakan Moneter Dalam Mengendalikan Inflasi di Indonesia dengan Pendekatan Error Correction Model (ECM)
Bibliographic record
Abstract
Inflation is one of the main problems facing Indonesia, and the effectiveness of policies in controlling inflation is still questionable because the findings of research analysis are still varied. Bank Indonesia as the central bank has made efforts to control inflation with various monetary policy instruments. Therefore, this study focuses on the effectiveness of monetary policy in controlling inflation in Indonesia. The data used in this study uses quarterly data covering the first quarter of 2010 to the fourth quarter of 2024. The research model used in this study uses the Error Correction Model (ECM) time series estimation. The results of the analysis found that interest rates and the amount of money in circulation as monetary policy instruments have not been effective enough in controlling inflation in both the short and long term. This is indicated by the results of the estimation of the significant but positive effect of interest rates on inflation in both the short and long term. While the amount of money in circulation has a significant negative effect in the long term and does not have a significant effect in the short term.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".