Milli Gelir Buyume Tahmini : IYA ve PMI Gostergelerinin Rolu
Bibliographic record
Abstract
[TR] Bir ceyrege iliskin milli gelir verileri gecikmeyle yayimlandigindan buyumeye iliskin zamanli tahmin uretme onem kazanmaktadir. Bu calismada, dinamik faktor modeli yontemi ile reel veriler ve anket gostergelerinden yararlanarak Turkiye ekonomisi icin donemlik milli gelir buyumesine iliskin tahminler elde edilmektedir. PMI ve IYA gibi anket gostergelerinin kullanilmasi ceyrek bitmeden yapilan tahminlerde kayda deger iyilesme saglamaktadir. [EN] GDP figures for a quarter are published with considerable delay. This increases the importance of producing timely forecasts for GDP growth. In this note, we forecast quarter-on-quarter GDP growth rate for Turkish economy using hard and soft data in a dynamic factor model. Using PMI and BTS indicators bring significant improvement in nowcasting performance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".