Extracting GDP Signals From the Monthly Indicator of Economic Activity: Evidence From Chilean Real-Time Data
Bibliographic record
Abstract
With real-time data it is analyzed what information Chile’s monthly indicator of economic activity (IMACEC) contains about the final GDP, defined as the growth rate that has been subject to at least two annual revisions. Data are presented and revisions briefly analyzed. It is argued that when three months of IMACEC data are available, it is possible to extract signals about the final GDP, which are as reliable as those contained in the first release of the growth rate. This result is obtained with the evaluation in-sample as well as out-of-sample. It is then investigated how much extra information IMACEC data provide of the final GDP compared to what is already present in historical data. The in-sample analysis indicates statistically significant improvements when more IMACEC data of the quarter are available. Measured by the root mean square nowcast error (RMSNE) the out-of-sample performance also improves as more monthly data are published, although when only the first IMACEC data of the quarter are available, this is not statistically significant.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".