Determinants of Thai Baht Exchange Rate and Asian Currencies Exchange Rate
Bibliographic record
Abstract
The study hypothesises that the exchange rate is a random walk series. Besides, the study incorporates main macroeconomic factors as a structural exchange rate determination. The Vector Error Correction Model (VECM) is applied in the model estimation for Thai baht. Moreover, the panel data model of the Asian exchange rate is estimated and analysed. The exchange rate of Thai baht is found to be a random walk process. The long-run equilibrium of the estimated cointegrating relation indicates all coefficients of the determining factors are statistically significant. An increase in the real interest rate and the foreign reserve has significant appreciation effects on the Thai baht. An increase in the income per capita has a significant depreciation effect on the Thai baht. External debt causes a depreciation in the Thai baht. The most substantial impact on the value of Thai baht is the income per capita. It follows by the foreign reserve, the real interest rate, and the external debt, respectively. During 2017 and 2018, the estimated exchange rate is appreciated by 4.21 per cent that is close to the actual appreciated value. The estimated Asian model is found consistent with the model of the Thai baht. The highest impact on the local Asian currencies is the income per capita. It follows by the foreign reserve and the real interest rate, respectively, with both quite close by their sizes. However, the foreign reserve has a more appreciated influence than that of the real interest rate for Thai baht.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".