Spurious Relationship of Long Memory Sequences in Presence of Trends Breaks
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Bibliographic record
Abstract
This article extends the theoretical analysis of spurious relationship and considers the situation where the deterministic components of the processes generating the individual series are long memory sequences with structural changes. Show it by using the ordinary least squares estimator, the t-statistics become divergent and pseudo correlation. However, two long memory time series having change points can produce spurious regression. In the presence of structural change points, confirm the rate of t-statistic tends to infinity increased with the increase in sample size. Numerical simulation results show that when structural changes are a feature of the data, the presence of spurious relationship is unambiguous. And the spurious regression not only depends on long memory indexes, but also for trend of model is also very sensitive.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.027 |
| Science and technology studies | 0.000 | 0.010 |
| Scholarly communication | 0.000 | 0.011 |
| Open science | 0.007 | 0.001 |
| 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 it