The Determinants of Sovereign Bond Yields in the EMU: New Empirical Evidence
Bibliographic record
Abstract
This paper investigates the determinants of sovereign bond yields in the case of ten Economic and Monetary Union (EMU) countries (five core economies and five peripheral countries) for the period of 2001-2015. To this end, we carry out a two-step methodology based on (i) a principal component analysis of the countries’ yields, which is aimed at splitting our sample into sub-periods, and (ii) a random forest model to investigate the determinants of bond yields in any identified sub-period enhanced with a variable selection process with simulated annealing. Our analysis indicates that macroeconomic fundamentals (especially the unemployment rate, the inflation rate and the government debt to the GDP change rate) are the main variables responsible for the sovereign bond yields in all the countries analyzed, both core and peripheral. In contrast, the bond yields do not seem to be intensively influenced by global indicators over the whole sampling period.
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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 | 0.001 | 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 | 0.001 | 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".