An Empirical Analysis of the Bond Market Behavior and Cointegration in the Selected APEC's Countries
Bibliographic record
Abstract
This article examines the behavior of Treasury bond rates in Asia-Pacific Economic Cooperation’s countries. Granger causality tests based on the vector error correction model (VECM) suggest bidirectional Granger causalities between changes in (i) the Canadian and Malaysian Treasury bond rates, (ii) the Canadian and New Zealand bond rates, (iii) the US and Malaysian Treasury bond rates, and (iv) the South Korean and Malaysian Treasury bond rates. The results also reveal unidirectional Granger causalities from changes in (i) the Canadian to US Treasury bond rates, (ii) the South Korean to New Zealand and US Treasury bond rates, (iii) the Malaysian to New Zealand and Thai Treasury bond rates, (iii) the Thai to New Zealand and South Korean Treasury bond rates, and (iv) the US to New Zealand Treasury bond rates. The Granger causality test based on the augmented vector autoregressive (VAR) procedure yields largely similar results from VECM. These empirical findings may be attributable to differences in economic policies, governance, culture, and other institutional arrangements in each of these countries. The empirical results are important for investors and traders since they can use past information in one country to predict prices and returns in the future in other countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".