A simple bootstrap test for time series regression models
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Bibliographic record
Abstract
Abstract In this paper, we consider a simple bootstrap test for testing a parametric regression functional form with time-series dependent data. We establish the asymptotic validity of the wild bootstrap method. Monte Carlo simulations show that the bootstrap test performs well based on 'wild bootstrap' critical values. Keywords: Dependent dataConsistent testWild bootstrapMonte Carlo simulation Acknowledgements I would like to thank the referee for his/her helpful comments. This research is partially supported by an internal research fund, University of Windsor. Notes †Hjellvik and Tjostheim Citation18 proposed to use [Lcirc](M 1) = n −1∑ t {[Mcirc] 1(Y t ) − γˆY t }2 w(Y t ) as a basis to test H 0, where γˆ is the least squares estimator of γ, [Mcirc] 1(x) is the nonparametric kernel estimator of E(Y t |Y t−1 = x) and w(x) is a trimming function.
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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 | 0.025 |
| 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 | 0.000 | 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