The Effect of Macroeconomic Variables on Macroprudential Indicators in Indonesia from the First Quarter of 2003 to the Fourth Quarter of 2013
Bibliographic record
Abstract
The macroprudential policy aims to mitigate the risk of financial systems to reduce the spread of negative impacts on macroeconomics. The macroprudential policy can be measured by using Financial Soundness Indicators (FSIs), including the Vector Error Correction Model (VECM) approach to analyse the influence and impact of a policy in the model. The variables in this study were modified into seven endogenous variables consisting of macroeconomic variables such as Consumer Price Index (CPI), Gross Domestic Product (GDP) growth, interest rate spread (IRS), lending interest rate (LIR) and foreign exchange reserves (DEV). Also, there were two macroprudential indicators, namely the ratio of Non-Performing Loans (NPL) and Capital Adequacy Ratio (CAR), which were divided into two models. This study aims to determine the significance and the effect of macroeconomic variables on macroprudential indicators in Indonesia during the first quarter of 2003 to the fourth quarter of 2013. Macroeconomic variables used include consumer price indices, GDP growth rate, interest rate spread, lending interest rate, and foreign exchange reserves. The result of this study indicates that the resilience of the financial system in Indonesia is maintained amid the economic slowdown/downturn. Therefore, it does not cause any systemic effect that disrupts the financial system stability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".