Exact Inference in Long-Horizon Predictive Quantile Regressions with an Application to Stock Returns
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We develop an exact and distribution-free procedure to test for quantile predictability at several prediction horizons and quantile levels jointly, while allowing for an endogenous predictive regressor with any degree of persistence. The approach proceeds by combining together the quantile regression t-statistics from each considered prediction horizon and quantile level, and uses Monte-Carlo resampling techniques to control the familywise error rate in finite samples. A simulation study confirms that the proposed inference procedure is indeed level-correct and that testing several quantile levels jointly can deliver more power to detect predictability. In an empirical application to excess stock returns, we find that the default yield spread predicts the right tail while the short-term interest rate predicts the center of the return distribution. This predictability evidence is stronger at shorter rather than longer horizons.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it