MétaCan
Menu
Back to cohort
Record W2151725484

The Comparative Analysis of Credit Risk Determinants In the Banking Sector of the Baltic States

2011· article· en· W2151725484 on OpenAlexvenueno aff
Grigori Fainštein, Igor Novikov, Ehitajate tee

Bibliographic record

VenueReview of Economics and Finance · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsReal estateLoanError correction modelPortfolioEconomicsNon-performing loanMonetary economicsEconometricsFinancial systemMacroeconomicsCointegrationFinancial economicsFinance
DOInot available

Abstract

fetched live from OpenAlex

A vector error correction model is applied to empirically investigate and compare the influence of macroeconomic and real estate market variables on the level of non-performing loans in the three Baltic States. A secondary goal is to analyze the effect of constant loan portfolio growth on the level of non-performing loans in the related countries. The research indicates that the most significant reason for the growth of non-performing loans was caused by the changes in the real GDP in all the three Baltic States. The increasing influence of rapid loan portfolio growth proves the assumption that banks underestimated the changes in the macroeconomic variables during the analyzed periods, especially in Latvia. Rapid growth of the real estate market played an important role in Latvia and Lithuania, but it was not as crucial as it has been previously assumed in Estonia.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.254
Teacher spread0.207 · 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 designObservational
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

Citations47
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueReview of Economics and FinanceSame topicBanking stability, regulation, efficiencyFrench-language works237,207