Tracking the Course of the Economy (Nowcasting of basic macroeconomic indicators of Slovakia)
Bibliographic record
Abstract
Real GDP and its structure are available within 70 days after the end of the reference quarter. By using leading indicators of higher frequency, it is possible to nowcast GDP in real-time. With an assumption of unobserved factor driving the business cycle we estimate dynamic factor models for real GDP, its demand components, inflation, wages and employment using statistically significant domestic and foreign indicators. To ensure the consistency of out-of-sample forecasts for GDP and its components, past forecast deviations and correlation coefficients are used to adjust the forecast, which helps to reduce the bias of individual models. Forecasts using real-time database are carried out since the 1st January of 2017 using daily data vintages. Real-time forecasts display a reduction of forecasting error with the arrival of new data in the last month of the quarter until the official publication. The main role of nowcasting in CBR is to track the actual positive and negative macroeconomic risks of the Slovak economy in relation to the latest official national macroeconomic forecast by the Macroeconomic Forecasting Committee. Additionally, the nowcast models help to improve precision of estimates of initial conditions of the economy by bridging the short-term forecast and mid-term forecast.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".