Законът на Оукън в България, Гърция и Русия: сравнителен анализ
Bibliographic record
Abstract
Целта на настоящата статия е да се извършат емпирична оценка и сравнителен анализ на Закона на Оукън за България, Гърция и Русия. Чрез регресия на времеви редове по метода на най-малките квадрати е моделирана връзката между безработицата, икономическия растеж и производствения разрив в България и Гърция за периода от първото тримесечие на 2000 г. до третото тримесечие на 2019 г., а в Русия – за интервала от първото тримесечие на 2003 г. до третото тримесечие на 2019 г. Резултатите от емпиричния анализ показват, че докато в България фазата от бизнес цикъла не влияе на валидността и силата на проявление на Закона на Оукън, то в Гърция и в Русия връзката между безработицата и съвкупния продукт е циклично обусловена – тя е много по-силна по време на спад, отколкото в период на подем. Okun’s Law in Bulgaria, Greece and Russia: A Comparative Analysis The purpose of the article is to perform an empirical assessment and comparative analysis of Okun’s Law for Bulgaria, Greece and Russia. Ordinary least squares regressions of time series data (from the first quarter of 2000 to the third quarter of 2019 in Bulgaria and Greece, and from the first quarter of 2003 to the third quarter of 2019 in Russia) are employed to estimate the relationships between unemployment, economic growth and the output gap. The results from the empirical analysis show that while in Bulgaria the phase of the business cycle does not affect the validity and strength of the manifestation of Okun’s Law, in Greece and Russia the link between unemployment and output is cyclically influenced – it is much stronger during contraction than it is during expansion.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".