Rethinking an old empirical puzzle: econometric evidence on the forward discount anomaly
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Bibliographic record
Abstract
Abstract Using both semiparametric and parametric estimation methods, this paper corroborates earlier findings of fractionally integrated behaviour in the forward premium. Two new explanations are also proposed to help reconcile earlier conflicting empirical evidence on the time series properties of the forward premium. Traditional regression approaches used to test the forward rate unbiasedness hypothesis are then evaluated, including regression in levels, in returns (Fama's, 1984 , regression), and in error‐correction format. Interesting statistical and/or interpretive implications are found in all three cases. For example, the predictions of the appropriate nonstandard limit theory are consistent with many of the standard empirical results reported from Fama's regression, including the commonly occurring, yet puzzling negative correlations between spot returns and the forward premium. It is suggested that the principal failure of unbiasedness, may be due instead to the difference in persistence between these two series. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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