Measuring financial stress and economic sensitivity in CEE countries
Bibliographic record
Abstract
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 reained 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.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".