Nowcasting to Predict Economic Activity in Real Time: The Cases of Belize and El Salvador
Bibliographic record
Abstract
This paper presents machine learning models fitted to nowcast or predict quarterly GDP activity in real time for Belize and El Salvador. The initiative is part of the Inter-American Development Bank's (IDB) ongoing effort to develop timely economic monitoring tools following the shock of the Covid-19 pandemic. Nowcasting techniques offer an effective tool to fill the information gap between the end of a quarter and the official publication of macroeconomic indicators that are generally lagged by 60 to 90 days, by exploiting the availability of other indicators that are published more frequently. The results show that machine learning techniques can produce accurate quarterly GDP forecasts for two structurally different economies within economic contexts marked by extreme degrees of volatility and uncertainty at both the national and international levels. Because the calibration of nowcasting exercises is a dynamic process that is refined over time, at the IDB, we trust that this document will help support the ongoing work of the governments and statistical agencies of Belize and El Salvador in securing better economic forecasts to inform agile policy decisions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".