Revisiting unemployment hysteresis in G7 countries: Evidence from the Fourier Nonlinear Quantile Unit Root approach
Bibliographic record
Abstract
Unemployment is a socio-economic issue, and as such, it remains a dynamic concern for economic policy authorities across all periods. The structural and cyclical aspect of unemployment determines the implementation direction of policies. This study seeks to reassess the unemployment hysteresis hypothesis in G7 countries from an asymmetric viewpoint and to present novel empirical findings. Both conventional and quantile unit root tests are utilized in the analysis. The primary focus of the study is the Fourier Nonlinear Quantile Unit Root (FNQUR) test, which simultaneously captures non-normal (heavy-tailed) distributions, nonlinearity, structural breaks, and asymmetric adjustments. Based on the findings of the Fourier ADF (FADF) test, two countries exhibit stationary processes, while the Fourier KSS (FKSS) test confirms stationarity for only one country. Similarly, the Fourier QKS (FQKS) test supports stationarity in two countries. However, the results of the FNQKS test, our main focus, differ significantly. Based on the results of the FNQKS test, Canada, Germany, Japan, the UK, and the US exhibit a unit root stationary process. These findings point to the validity of the Natural Rate Hypothesis (NRH). Accordingly, while conventional tests generally support the Unemployment Hysteresis Hypothesis (UH), the FNQKS test, contrarily points toward the NRH. An examination of the quantile-based results reveals that the impacts of positive and negative shocks diverge. In this context, the quantile results provide evidence for the presence of an asymmetric structure in Canada, Germany, Japan, the UK, and the US. This provides new evidence on unemployment hysteresis in the framework of G7 economies.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".