MétaCan
Menu
Back to cohort
Record W3121798503

Measuring financial stress and economic sensitivity in CEE countries

2014· article· en· W3121798503 on OpenAlex
Maciej Krzak, Grzegorz Poniatowski, Katarzyna Wąsik

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCASE Network Studies and Analyses · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsIndex (typography)RecessionEconomicsAccessionVulnerability (computing)Quarter (Canadian coin)Economic indicatorFinancial crisisSample (material)Vulnerability indexInternational economicsEuropean unionMacroeconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

This report presents the methodology for the construction of the Financial Stress Index (FSI) and the Economic Sensitivity Index (ESI) and investigates the economic situation in twelve Central and East European Countries (CEECs) between 2001 and 2012. The objective of this paper is to capture key features of financial and economic vulnerability and examine the co-movement of economic turmoil and financial disturbances that strongly affected the CEECs in the last decade. Our main finding is that the FSI can be used as a leading indicator and can be used to recognize changing trends in the index. A shift in the value of the index proves that EU accession has a positive, but minor influence on financial stability in the CEECs. On the other hand, the impact of the introduction of the euro in Estonia, Slovakia and Slovenia is ambiguous. For most of the countries in our sample, in 2007, the FSI started to grow rapidly, reaching its peak around the third quarter of 2008. Consequently, financial stress remained high for a few quarters and started to fall gradually. For a number of countries, we observe higher financial stress in the latest period of our analysis, i.e. 2010-2012. However, the value of the FSI was significantly lower than three years earlier. The results show that indices might be helpful in predicting future recessions. However, forecasting properties seem to be limited at this stage of our work.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.841

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 communication0.0000.000
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 head0.094
GPT teacher head0.289
Teacher spread0.195 · 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