A simple approach to nowcasting GDP growth in CESEE economies
Bibliographic record
Abstract
Given the publication time lag inherent in national accounts data, we explore the informational content of higher-frequency indicators that become available during a quarter in nowcasting current-quarter GDP growth rates for 11 Central, Eastern and Southeastern European (CESEE) economies. Building on recent findings, we restrict our choice to three model classes: (1) principal component models, (2) bridge equations and (3) simple autoregressive (AR) models without higher-frequency variables. Moreover, we propose a variety of forecast combinations to arrive at the highest possible forecast accuracy. Our estimation sample starts in the first quarter of 2003, and our evaluation period ranges from the second quarter of 2012 to the fourth quarter of 2017. We find that higher-frequency indicators contain useful information for predicting current economic activity in most of the economies in our sample. Using forecast combinations of models with and without higher-frequency variables yields additional gains in predictive accuracy. The best performers ultimately selected vary strongly across countries: we find 10 different models for 11 countries. Eight country models produce a statistically significantly smaller forecast error than the benchmark. Calculating a CESEE-11 country aggregate based on the individual country forecasts yields a forecast performance that is highly superior to that of the benchmark.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".