Exchange Rate Pass-Through into Import Prices: Empirical Evidences from Major Southeast Asian Countries
Bibliographic record
Abstract
Most of the empirical studies on exchange rate pass-through focus on industrialized countries, and only a few studies have been done for developing countries. In this paper we estimate exchange rate pass-through for four Southeast Asian countries: Indonesia, the Philippines, Singapore and Thailand, by employing cointegration analysis and Error Correction Mechanism. The results of the estimation using quarterly data show that the long run exchange rate pass-through into import prices for Indonesia, the Philippines, Singapore, and Thailand are 0.983, 1.179, 0.200, and 0.800, respectively. When we use monthly data, the estimates of the long run exchange rate pass-through are 0.885, 1.529, 0.109, and 0.396 for Indonesia, the Philippines, Singapore, and Thailand, respectively. To compare exchange rate pass-through in Southeast countries with those of industrialized countries we estimate the exchange rate pass-through of Australia, Canada, and New Zealand. The exchange rate pass-through of Southeast Asian countries do not have systematic difference with the exchange rate pass-through of the sample of industrialized countries. Macro variables that appear to contribute to the variation of exchange rate pass-through across countries sample are inflation and money growth. From micro side, the presence MNCs together with intra-firm trade seems to have contribution for the variation of exchange rate pass-through across countries.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".