ANALYSIS DETERMINANTS THE VELOCITY OF MONEY IN INDONESIA
Bibliographic record
Abstract
This study aims to analyze the effect of gross domestic product, inflation, and interest rates on the velocity of money in Indonesia for the period 2011 quarter I to 2020 quarter IV. This type of research is descriptive research with a quantitative approach. Research data was obtained from publications by the Central Bureau of Statistics (BPS) and Bank Indonesia. The data analysis technique used Multiple Linear Regression using Eviews 12. The results stated that the gross dometic product variable partially had a negative and significant effect on velocity of money, this was indicated by the value of t count > t table, which is -4.002220 > 2.021075, while the inflation variable has no effect on velocity of money with the value of t calculate < t table, which is -0.084848 < 2.021075 and the variable interest rate has a positive effect on velocity of money with the value of t calculate > t table, which is 3.200801 > 2.021075. Simultaneously (together) gross domestic product, inflation, and interest rates have a significant effect on the velocity of money in Indonesia with f calculated > f table which is 27.04354 > 2.838745.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".