Does Political Risk Matter for Economic Growth in Cyprus?
Bibliographic record
Abstract
This study aims to capture the co-movement between political risk and economic growth in Cyprus employing quarterly data over the time period between 1995Q1 and 2018Q4, adopting a wavelet coherence technique allowing to investigate both the short and long run causal link between economic growth and political risk in Cyprus. The outcomes obtained expose that (i) economic growth and political risk were significantly vulnerable in 2000 and 2004 at varying frequencies; (ii) political risk was an important factor for predicting economic growth in Cyprus between 2006 and 2018; (iii) economic growth causes political risk in Cyprus in the short term between 2006 and 2009. In addition, it is noteworthy that causality tests of gradual–shift and Toda-Yamamoto are utilized to verify the significance of political risk for predicting economic growth in Cyprus.
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How this classification was reachedexpand
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".