The Relationship between Average Annual and Quarter-On-Quarter GDP Growth Rates: Implications for Projections and Macroeconomic Analysis
Bibliographic record
Abstract
The average annual growth rate of GDP can be formulated algebraically as a weighted average of the quarter-on-quarter growth rates of the preceding and the current year. Sometimes this can give rise to counterintuitive results and misinterpretations of how the economy is evolving. For example, a given sequence of GDP growth rates, in quarter-on-quarter terms, in the current year, may give rise to very different average annual rates depending on the trajectory of GDP in the preceding year. Also, with given quarter-on-quarter growth figures for the four quarters of a particular year, average GDP growth will be higher, the earlier in the year that the largest quarteron-quarter increases occur. In the context of macroeconomic projections, analysis tends to focus on average annual GDP growth rates, insofar as they offer a summarised version of the outlook. However, it should be noted that revisions to the current year’s projections with a particular sign (for example, upwards) may reflect changes of two types: first, the publication of new, more favourable National Accounts data for past quarters; and second, a downward revision to growth prospects for the remaining quarters of the year. Therefore, it would be a mistake to conclude from the mere observation of an upward revision to average annual growth that the economic outlook has improved.
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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 | 0.012 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".