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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.012 |
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".