The Puzzle of Weak First-Quarter GDP Growth
Bibliographic record
Abstract
The official estimate of real GDP growth for the first three months of 2015 was shockingly weak. However, such estimates in the past appear to have understated first-quarter growth fairly consistently, even though they are adjusted to try to account for seasonal patterns. Applying a second round of seasonal adjustment corrects this residual seasonality. After this correction, aggregate output grew much faster in the first quarter than reported. In late April, the Bureau of Economic Analysis (BEA) released its initial estimate of U.S. economic growth for the first three months of 2015. The report was very disappointing, as inflation-adjusted, or real, gross domestic product (GDP) edged up a mere 0.2 % at an annual rate in the first quarter. This estimate was far weaker than many economists had forecast, and it raised concerns that the underlying economic recovery may have stalled. Such anemic growth is of particular concern to Federal Reserve policymakers considering when to begin normalizing monetary policy. However, a number of analysts have suggested that the reported weakness in first-quarter growth may have been exaggerated by a statistical anomaly (see, for example, Liesman 2015 and Wolfers 2015). Indeed, an unusual pattern has prevailed for some time in which first-quarter real GDP growth is generally lower than growth later in the year. This regular, calendar-based statistical pattern is a puzzle because the
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".