Monetary policy synchronization in the ASEAN-5 region: an exchange rate perspective
Bibliographic record
Abstract
In light of the long-standing vision of economic and monetary integration in the ASEAN (Association of Southeast Asian Nations) region and the importance of coordinating monetary policies to achieve it, the objective of this article is to assess the monetary policy synchronization among the founding members of the ASEAN, that is, Indonesia, Malaysia, the Philippines, Singapore and Thailand. Due to the importance of exchange rate movements to monetary policies, we approach this issue from a currency exchange rate perspective. Specifically, multivariate trend–cycle decomposition is employed to investigate common trends and common cycles among the exchange rates of these countries during the period 1976–2012. Our analysis reveals that the real exchange rates of Malaysia, the Philippines, Singapore and Thailand share common cycles in the short term and have common trends in the long term, but the Indonesian currency does not share these relationships. Thus, our results augur well for the synchronization of monetary policies among Malaysia, the Philippines, Singapore and Thailand. In contrast, the relatively turbulent dynamics of the Indonesian rupiah evident in frequent bouts of stark depreciation separated by periods of steady depreciation over the past three decades raise questions regarding the readiness of Indonesia for participating in a monetary alliance with the ASEAN-4 nations.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".