Output Gap and GDP in Brazil: a real-time data analysis
Bibliographic record
Abstract
Economic agents make decisions using real-time data. However, recent literature has shown that several economic activity measures go through important revisions over time, impairing the reliability of real-time data. We organize a real-time dataset for Brazil’s GDP, and assess the revisions of GDP growth and the output gap. We show that GDP growth revisions are substantial, with a 0.7 p.p. mean absolute revision for the quarter-over-quarter growth, although the revisions become less important for four-quarter changes. To assess output gap revisions, we use four methods to estimate the output gap: Hodrick-Prescott filter, linear trend, quadratic trend, and Harvey-Clark model of unobservable components. The output gap revisions are substantial in all methods, with absolute mean revisions between 0.6 p.p. and 2.3 p.p. In three out of the four methods, the revisions implied changes in the output gap sign in 30 percent or more of the cases. In general, both the GDP data revision and the sample increase are relevant sources of output gap revisions. Key words: Real-time data; Output gap; Gross Domestic Product; Business cycle; Brazil. JEL classification: C82; E32.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".