An ARDL Analysis Of The Exchange Rates Principal Determinants: ASEAN-5 Aligned with The Yen
Bibliographic record
Abstract
This study examines an empirical analysis of long-run and short-run forcing variables of purchasing power parity (PPP) for ASEAN-5 currencies: Malaysian Ringgit, Indonesian Rupiah, the Philippines Peso, Thailand Bath, and Singapore Dollar, against the Japanese Yen, i.e., their real exchange rate (RER). This study uses a recently developed autoregressive distributed lag (ARDL) approach to co-integration (Pesaran et al., 2001) over the period 1991:Q1 – 2006:Q2. Our empirical results point out that the domestic money supply (M1) is the significant long run forcing variable of PPP for ASEAN-5 RER’s for the study periods. However, in the short- run the impact of variables have different impact during the sub-periods and full period for ASEAN-5 countries, the results suggest that the domestic money supply (M1) for Malaysia, Indonesia, Philippines ,and Singapore respectively, , have the highest significant short run forcing variable of PPP for countries RER’s. However, foreign interest rates followed by domestic money supply are the short-run forcing variables for Thailand’s RER. This may be due to the peculiarity of Thailand government’s management of the Asian Financial Crisis (AFC).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.009 | 0.001 |
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".