Finite Sample Behaviour of the Level Shift Model using Quasi-Differenced Data
Bibliographic record
Abstract
When using quasi-differenced data in a model where a break in the intercept is allowed, asymptotic distributions of the M, ADF, and PT statistics are the same as those in the model where only an intercept and a time trend are included. However, the finite sample behaviour for common sample sizes used in empirical applications, is very different. I calculate finite-sample critical values using two different methods to select the break point and the lag length. Comparison with asymptotic and finite-sample distributions of a model where only a constant and a time trend are included in the set of deterministic components show strong differences. In particular these differences are more clear for the M and PT statistics, while the ADF statistics is not affected too much. An empirical application is performed to show the pitfalls of using asymptotic or finite-sample critical values for a model where a break in the intercept has not been taken into account.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".