The Impacts of Fiscal and Macroeconomic Factors on Vietnam Government Bond Yield
Bibliographic record
Abstract
Government bond yield refers to the borrowing cost for government and the expected return for the individual and institutional investors. Having knowledge of government bond yield helps government operate or adjust the government bond issuance to boost the economic conditions in a country and support investors when diversifying their investment portfolio. To contribute to government bond’s literature and government’s policy, the determinants of government bond yield in Vietnam are examined by using GARCH-types models for time-series data. The findings show that for the 3-year and 5-year government bonds, there are positive relationships between the percentage change of Central Government Balance, Policy Rate change and government bond yields change; while the percentage change of Exchange Rate and VN Index negatively affect government bond yields change. For 10-year government bond, Policy Rate, VN Index, Inflation and VIX are the most significant determinants of the government bond yields. Their changes positively affect bond yields change while Inflation has a negative relationship with government bond yields change. Moreover, Inflation has more significant impact on the change in long-term government bond yields than that in shorter-term government bond yields.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".