The Influence of Exchange Rate and Foreign Capital on the Performance of Inflation Targeting Framework in Indonesia
Bibliographic record
Abstract
This study provides empirical evidence on the problem of the trilemma of monetary policy in an open economy, in the context of the effect of exchange rates and foreign capital flows on the performance of the ITF in Indonesia. The method used as an empirical estimate is the Structural Vector Autoregressive (SVAR) model. This model allows to include restrictions in the empirical estimation of parameters that measure the contemporaneous effect of one variable on another variable according to the structure of the macroeconomic model. Meanwhile, the lagged effects are estimated according to the VAR model. Therefore, the SVAR model is considered more appropriate than the ordinary VAR model because it can measure both the instantaneous effect and the intertemporal effect of the problem under study. The SVAR model uses restrictions that are consistent with the theoretical model in its estimation, regardless of the time-to-time effect of one variable on another. There are 9 variables in the SVAR model, namely: global risk, oil prices, federal funds rate, economic growth, inflation, interest rates, monetary policy, credit interest rates, foreign portfolio investment flows, and the rupiah exchange rate. All data used were obtained from several sources, including: Bank Indonesia, Central Statistics Agency, and IMF. Based on the estimation results, the exchange rate and foreign capital flows have a significant effect on inflation and economic growth, thus affecting the performance of the ITF in Indonesia. In particular, there is a relative influence between external factors, particularly global commodity prices, US monetary policy interest rates, and global risks, and domestic factors, particularly economic growth, monetary policy interest rates, and bank interest or credit rates. This study also concludes that in addition to inflation and economic growth considerations, Bank Indonesia also considers exchange rate movements in determining its interest rate policy response.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".