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Record W2536358153

Credit risk modelling: default probabilities for Portuguese municipalities

2016· preprint· en· W2536358153 on OpenAlexfundno aff
Karel Janda, David Moreira

Bibliographic record

VenueMunich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 2016
Typepreprint
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
FundersGrantová Agentura České RepublikyEuropean CommissionMcGill University
KeywordsDefaultPortugueseDebtFinancial stabilitySample (material)BusinessCredit riskProbability of defaultDebt service coverage ratioEconomicsFinancial systemFinanceExternal debt
DOInot available

Abstract

fetched live from OpenAlex

This paper investigates whether the economic adjustment program and post-program surveillance contributed positively to the structural recover of the Portuguese economy. This examination was conducted observing evidences related to several objectives included in the programs and on a sample of data collected from local defaulted and non-defaulted municipalities. Our results confirm that the financial aid provided by EC, ECB, and IMF had a small impact on the internal economic adjustment. There is a residual positive impact from the implemented reforms to promote growth, jobs, public debt, deficit, and stability of the country’s financial sector. The evidence is revealed by the measurement of the key program achievements and by the indicators related to the current high default probabilities of a large number of local municipalities.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.059
GPT teacher head0.279
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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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