On Combining Evidence from Heteroskedasticity Robust Panel Unit Root Tests in Pooled Regressions
Bibliographic record
Abstract
Volatility break robust panel unit root tests (PURTs) recently proposed by Herwartz and Siedenburg (Computational Statistics & Data Analysis 2008, 53, 137–150) and Demetrescu and Hanck (Econometrics Letters 2012, 117, 10–13) have different performances under both the null and local alternatives. Common practice in empirical research is to apply multiple tests if none is uniformly superior. We show that this approach tends to produce contradictory evidence for the tests considered, making it unclear whether to reject the null. To address this problem, we advocate a combined testing procedure. Simulation evidence shows that the combined test has good size control and closely tracks the more powerful test. An empirical application reinvestigates whether there is a unit root in OECD inflation rates. We find evidence that inflation is stationary for long observation periods, but we cannot reject nonstationarity in most subsets of countries for the last three decades.
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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 | 0.066 | 0.332 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".