Uncertainty measures and inflation dynamics in selected global players: a wavelet approach
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Bibliographic record
Abstract
This study investigates the dynamic relationship between economic policy uncertainty (EPU), geopolitical risks (GPR), the interaction of EPU and GPR (EPGR), and inflation in the USA, Canada, the UK, Japan, and China. We employ the continuous wavelet transform (CWT) to track the evolution of model variables and the wavelet coherence (WC) to examine the co-movement and lead-lag status of the series across different frequencies and time. To strengthen the WC, we apply the multiple wavelet coherence (MWC) to determine how good the linear combination of independent variables co-moves with inflation across various time-frequency domains. The CWT reveals heterogeneous characteristics in the evolution of each variable across frequencies. Inflation across samples shows strong variance in the short-term and medium-term while the volatility fizzles out in the long-term. For the explanatory variables, a similar pattern holds for EPU except for Japan and China, where coherence is evident in the short-term. The USA's and Canada's GPR reveal strong coherence in the short- and medium-term. Also, the UK and China reflect strong coherence in the short-term but weak significance in the medium-term, while Japan's GPR reflects only strong coherence in the short-term. The EPGR shows strong variation in the short-and-medium-term in the samples except in China. The WC's phase-difference reflects bidirectional causalities and switches in signs among series across different scales and periods in the samples, while the MWC reveals the combined intensity, strength, and significance of both EPU and GPR in predicting inflation across frequency bands among the countries. Findings also show significant co-movement among series at date-stamped periods, corroborating critical global events such as the Asian financial crisis, Global financial crisis, and COVID-19 pandemic. The paper has policy implications.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it