Bibliographic record
Abstract
There have been many attempts at solving the problem of determining the “fundamental value” of the credit spread of a government bond. This is particularly important in the case of Eurozone, where the ECB intervention on the government bonds’ market is allowed only if the “spread” paid by the sovereign issuer is higher than the one justified by “fundamentals”. The complication in determining what is a fair level of the spread stems from the fact that public debt sustainability depends on many factors, among them the level of interest rates paid. This sort of circularity between debt sustainability and interest rate paid by the sovereign issuer is the major source of complexity. This paper highlights a possible solution inside a simplified framework resembling the peculiar institutional settings of the Eurozone: no possibility of money-financing, the famous Maastricht Treaty 3%-60% parameters, availability of financial assistance program subordinated to the acceptance of consolidation plans for public finances. We obtain the possibility of multiple equilibria for the credit spread, whose stability can be analyzed through a phase diagram. The dynamics of the model is derived from probabilistic assumptions about the public debt process. It does not depend on “loss” functions devised to model the strategic relationship between debtors and creditors, as in previous literature on public debt sustainability. Dynamic properties of equilibria can be used to gain insight on what does it mean “good” or “bad” equilibrium from the perspective of the ECB.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".