On the optimal number of indicators – nowcasting GDP growth in CESEE
Bibliographic record
Abstract
We employ principal components and dynamic factor models for nowcasting GDP growth in selected Central, Eastern and Southeastern European (CESEE) economies. Our estimation sample extends from the first quarter of 2000 to the second quarter of 2008, our evaluation period from the third quarter of 2008 to the third quarter of 2014. For this period, we produce quasi out-of-sample forecasts of past-, current- and next-quarter GDP growth for seven CESEE economies. The models differ with respect to the estimation method, model specification, and the number of short-term indicators used. We find, first of all, a clear gain in predictive accuracy from using a nowcasting model with monthly indicators compared to the naïve benchmark. Furthermore, for our sample of small, open economies, we find that models using a smaller set of carefully selected indicators yield lower prediction errors on average than models based on larger information sets. Finally, we identify a clear gain in forecast performance from including foreign or euro area indicators.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".