Corrigendum for Dividend Dynamics, Learning, and Expected Stock Index Returns
Bibliographic record
Abstract
We discovered inconsistencies in our coding for Jagannathan and Liu (2019) that, after addressed, has led to changes in the tables and figures that we reported. These changes do not in anyway affect any of the paper's statements, findings, or conclusions. We report updated tables and figures in this erratum and highlight any statistics where the change is nontrivial by underlining it. The nontrivial changes are as follows. In Table III (and Table V), the out-of-sample R2 for our dividend model drops from 0.413 (0.395) to 0.320 (0.331), but remains statistically higher than the corresponding R2s of competing models. Return predictability R2 for the full learning model in Table IX increases from 0.271 to 0.291 for the full data sample, in Table XI it increases during expansions from 0.191 to 0.252, and decreases during recessions from 0.641 to 0.474. These changes do not change the main conclusions in the paper. The Internet Appendix to the paper gives the data, and the Matlab and Stata codes used in generating the tables and figures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".