Датиране и прогнозиране на икономическия цикъл на България
Bibliographic record
Abstract
Целта на настоящото изслед­ване е да датира икономическия цикъл на България и да създаде модел за прогнозира­нето му. Обект на изследването са детер­минантите на фазата на икономическия цикъл на България за периода от първото тримесечие на 2003 до третото триме­сечие на 2015 г. Предмет на изследване е въздействието на лихвения спред, цените на петрола, новоиздадените разрешител­ни за строителство, индекса Sofix, до­верието в промишления сектор и БВП на страните от Еврозоната върху фазата на икономическия цикъл. Използвана е ме­тодологията логистична регресия на вре­меви редове. Всички променливи оказват статистическо значимо влияние върху фа­зата на икономическия цикъл и посоката на това влияние съответства на теоре­тичните очаквания. Dating and Forecasting Bulgaria’s Business Cycle The goal of this article is to date Bulgaria’s business cycle and create a model for forecast­ing it. The objects of the article are the determi­nants of Bulgaria’s business cycle phases from the first quarter of 2000 to the second quarter of 2016. The subject of the study is the influ­ence of the interest spread, oil prices, building permits, the stock market index Sofix, the con­fidence in industrial sector and the GDP of the euro area countries on business cycle phases. The methodology logistic regression of time se­ries has been used. All variables have a signifi­cant impact on business cycle phases and the direction of impact corresponds to theoretical expectations.
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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 | 0.019 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.048 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 0.194 |
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; both teacher heads agree on what is shown here.
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".