Residual Seasonality and Monetary Policy
Bibliographic record
Abstract
Much recent discussion has suggested that the official real GDP data are inadequately adjusted for recurring seasonal fluctuations. A similar pattern of insufficient seasonal adjustment also affects the published data for a key measure of price inflation. Still, such residual seasonality in the published output and inflation statistics is unlikely to mislead Federal Reserve policymakers or adversely affect the setting of monetary policy. Almost all economic data exhibit seasonal fluctuations—changes that occur around the same time each year due to such things as normal weather variation and holiday schedules. Because such variation obscures the underlying cyclical movements of the economy, most economic data are reported on a seasonally adjusted basis. Recently, a number of commentators have argued that the seasonal adjustment of real GDP by the Bureau of Economic Analysis (BEA) is incomplete and understates growth early in the year (see, for example, Rudebusch, Wilson, and Mahedy 2015). Indeed, since 1990, average real GDP growth has been significantly slower during the first three months of each year than in the subsequent quarters. Such “residual seasonality ” in the published seasonally adjusted real GDP data should be taken into account when assessing the state of the economy. Accordingly, the very weak readings on real GDP for the first quarter of this year have been a concern to Federal Reserve policymakers as they try to discern
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".