Bibliographic record
Abstract
The purpose of this paper is to find the determinants of cyclical real aggregate dividends. In the literature, dividends are hypothesized to be proportional to real permanent earnings, with a smoothing factor that is between zero and +1. An additional postulate is that dividends adjust to a target dividend payout ratio. Managers will only change dividends if they can be sure that permanent earnings have increased. This allows for the payout ratio to be persistent and avoids reversing the payout decision if temporary earnings fall. The contribution of this paper is six-fold. The first is to generate cyclical changes of the variables by an appropriate filtering rule, a rule that is a common usage in macroeconomics. The second is to consider two proxies for real permanent earnings: real stock market prices, keeping real interest rates constant, and long term real interest rates, keeping market prices constant. The third is to adjust the estimation procedure for conditional heteroscedasticity. The fourth is to test whether transitory real earnings have an impact on dividends. The fifth is to find out if there are symmetrical effects of positive and negative earnings shocks. The last is to carry out stability tests over different time periods. One of the major findings is that, the three independent variables--stock market prices, interest rates, and transitory earnings, all have a significant effect on dividends, and that the smoothing factor is surprisingly the same for all three independent variables.
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".