The Interaction Between Monetary and Macroprudential Policy to Achieve Price and Financial Stability: An Evidence from Indonesia
Bibliographic record
Abstract
The 1998 Asian Financial Crisis was a milestone in the existence of structural policy reforms in the Indonesian financial sector. Most of the empirical results show that the financial crisis was caused by the lack of soundness and instability of the financial sector. This problem changed the perspective of Bank Indonesia, the central bank in Indonesia, that financial stability is as important as price stability. This highlights the need for the central bank to also ensure financial stability, while monetary policy focuses on price stability and economic growth. However, achieving these goals does not always ensure financial stability. To address systemic risk, Indonesia has begun adopting macroprudential policies. Thus, monetary policy cannot secure both price and financial stability, and a policy mix with macroprudential measures is needed to achieve both price and financial stability. This research examines the relationship between monetary and macroprudential policies and their effects on stability. Monetary policy was measured by the BI Rate and macroprudential policy was measured by Loan to Value (LTV). Price stability was proxied by inflation, and financial stability by credit growth. We analyzed the causality between the variables using the Vector Autoregression Model (VAR) and Granger Causality Test, using quarterly time series data from 2005:Q1 to 2018:Q3. The findings indicate that monetary and macroprudential policies significantly affect price and financial stability. Empirical findings show that tightening the BI rate and LTV significantly reduces inflation and credit growth. This paper highlights the need for a policy mix to ensure price and financial stability.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".