Specification Error, Estimation Risk, and Conditional Portfolio Rules
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In characterizing the data‐generating process for excess returns, an investor faces both parameter uncertainty (or “estimation risk”) and specification error. We examine the trade‐off between these two effects, in the context of an optimal consumption/portfolio decision problem, by considering a minimal extension of the standard assumption of a linear vector autoregression for excess returns. The key additional assumption in our data‐generating process is a positive linear relationship between market volatility and lagged market dividend yields. This simple specification is consistent with a long sample of U.S. data. We show that volatility adjusted rules are substantially less sensitive to variation in dividend yields, and volatility‐related specification error is economically significant – even when the decisions are based on sample estimates from data sets of a realistic size.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it