Reserve Bank of Australia The Long and Large Decline in U.S. Output Volatility
Bibliographic record
Abstract
second started in 1991 and, although showing signs of faltering, has recorded its fortieth quarter as this volume goes to press and is already the longest U.S. expansion on record. One view is that these two long expansions are simply the result of luck, of an absence of major adverse shocks over the last twenty years. We argue that more has been at work, namely, a large underlying decline in output volatility. Furthermore, we contend, this decline is not a recent development—the by-product of a “New Economy ” or of Alan Greenspan’s talent. Rather it has been a steady decline over several decades, which started in the 1950s (or earlier, but lack of consistent data makes this difficult to establish), was interrupted in the 1970s and early 1980s, and returned to trend in the late 1980s and the 1990s. 1 The magnitude of the decline is substantial: the standard deviation of quarterly output growth has declined by a factor of three over the period. This is more than enough to account for the increased length of expansions. We thank Benjamin Friedman for his comments, as well as Robert Solow and participants in the MIT macro lunch. This paper builds on John Simon’s doctoral thesis (Simon, 2000). The views expressed are our own and should not be attributed to the Reserve Bank of
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.095 | 0.037 |
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".