Unit root tests and structural change when the initial observation is drawn from its unconditional distribution
Bibliographic record
Abstract
Following Elliott (1999; International Economic Review 40, 767–83.) andPerron and Rodríguez (2003; Journal of Econometrics 115,1–27), we develop unit root tests in the context of structural change models using GLS detrended data (Elliott, Rothenberg and Stock 1996; Econometrica 64, 813–39) when the initial observation is drawn from its unconditional distribution. We derive the limiting distributions of the M‐tests (Stock, 1999 cointegration, causality and forecasting; Oxford University Press, 137–67; Perron and Ng 1996; Review of Economics Studies 63, 435–463), the ADF statistic and a feasible optimal point test from which we derive the power envelope. Asymptotic power functions are calculated and compared with the case where the initial condition is not random. Finite sample size and power simulations under various forms of error processes are performed using different lag selection methods and two different methods to select the break point. Empirical applications are also provided.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".