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Record W2529983431 · doi:10.12735/jfe.v4n4p11

Monetary Union Dynamics with Unsustainable Public Debt

2016· article· en· W2529983431 on OpenAlexvenueno aff
Marcello Esposito

Bibliographic record

VenueJournal of Finance & Economics · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEuropean Monetary and Fiscal Policies
Canadian institutionsnot available
Fundersnot available
KeywordsDebtEconomicsMonetary economicsDynamics (music)International economicsMacroeconomicsPsychology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.173
Teacher spread0.158 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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