Bibliographic record
Abstract
Even though the behavior of the U.S. profit growth varies over the economic cycle that variation itself drives investor behavior and asset prices. We raise three fundamental questions which are; first, does profit growth over time exhibit a mean-reverting behavior? Second, how volatile are profits and does this volatility obscure the message of profit growth? Finally, do profit growth rates vary between decades/ sub-samples?Our efforts suggest that since 1970 the mean and standard deviation of profit growth had actually been decreasing up until 1990s. For the most recent (2000-08) period, the profit growth shows an up-tick in both the mean and the standard deviation. For the entire period, 1970-2008, we find that the trend coefficient is statistically insignificant.We apply the traditional unit root tests, efficient unit root tests, and unit root tests with structural break on the profits series. In addition, we follow Hamilton’s approach and apply an ARCH approach on the profits series.Our empirical findings are consistent with the Schumpeter’s view, mean-diversion with a possibility of deviation from long-run trend growth. In addition to the factors introduced by Schumpeter there may be some exogenous shocks which could alter the long-run path of the profits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".