Coherence of the business cycles of prospective members of the euro area and the euro area business cycle
Why this work is in the frame
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Bibliographic record
Abstract
Is it beneficial for Central and Eastern European EU Member States to join the euro area? To answer that question, the coherence of the business cycles of six EU Member States and the euro area is analyzed. These countries recently joined (Croatia) or are supposed to join the euro area in the (near) future. The analysis utilizes the synchronicity and similarity measures proposed by Mink et al. (2012) . Whereas the synchronicity measure captures whether output gaps have the same sign, the similarity measure identifies differences in cycle amplitudes. It is observed that the business cycles of several countries, notably Romania and Hungary, are out of sync with that of the euro area. The output gap similarity and synchronicity measures for Croatia are also fairly low. However, this also holds for some countries in the euro area. • We analyse business cycles of six Eastern-European EU Member States and the euro area. • The analysis utilizes the synchronicity and similarity measures of Mink et al. (2012). • These measures capture differences in the sign and amplitude of output gaps. • Business cycles in Romania and Hungary are out of sync with that of the euro area. • Also the output gap similarity and synchronicity measures for Croatia are fairly low.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it