Nowcasting GDP Growth: statistical models versus professional analysts
Bibliographic record
Abstract
This thesis contains four chapters that cast new light on the ability of professional analysts and statistical models to assess economic growth in the current quarter (nowcast) and its development in the near future. This is not a trivial issue. An accurate assessment of the current state of the economy is important as starting point for medium-term forecasts, especially during times of heightened volatility, such as the recent financial crisis. \\n \\nNowadays, practitioners have a wealth of statistical model to choose from; but which one should they use? Can statistical models be modified to improve their forecasting accuracy? What are the gains from combining the forecasts of different statistical models? Did the financial crisis change the forecasting performance of statistical models relative to professional analysts? Can practitioners use the near-term outlook of professional analysts to improve the forecasting accuracy of statistical models? This thesis gives answers to these questions, providing new insights of interest to both academics and practitioners. Central to this research is the construction of a new dataset, comprised of the near-term economic growth forecast of professional analysts, and the monthly indicators available when analysts made their forecasts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".