Panel Data Stochastic Convergence Analysis In OECD Countries: Evidence From Panel Unit Root Tests
Bibliographic record
Abstract
This study investigates the stochastic convergence behaviour of real GDP per capita for the period 1948-2010 and 1900-2010 across the 20 OECD countries. For that purpose, the existence of stochastic convergence is estimated by technique basedon the Fourier function approach (FKPSS) stationary test developed by Becker, Enders and Lee(2006). As well as, Fouier IPS test was applied for the whole panel. The Fourier KPSS stationarity test results produced different results for both periods. While the results of the analysis strongly supported the stochaticcon vergence for all countires except Austria and Belgium for the period 1948-2010, it only supported for Austria, Canada, Fnland, France, Netherlands, New Zealand, Norway, Sweden, Switzerland, UK and USA during 1900-2010. The Fourier IPS test results for the entire panel support a stochactic on vergence for both periods. It is also observed that the Standart deviation values for o-convergence tent to decrease for the whole period. According to the results obtained, it is the most important finding of this study that the differences in real per capita incomes during the post-war period are largely eliminated in the oECD countries and that long-term country-specific shocks have a temporary effect on real per capita income.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".