Conditional effects of local and global risk factors on the co-movements between economic growth and inflation: Insights into G8 economies
Bibliographic record
Abstract
World economies have experienced rise in uncertainties which has caused misalignments in the already existing nexus between inflation and economic growth. In addition to this, the presence of nonlinearities, asymmetry, heterogeneity, and structural shocks in time series data concerning substantial fluctuations that span systemic crises have rendered time and/or frequency connectedness worthy of investigation. Due to limited studies in this regard, the authors investigated the risk synchronisation among Gross Domestic Product (GDP), Consumer Price Index (CPI), Economic Policy Uncertainty (EPU) and Geopolitical Risk with insights from G8 countries. To achieve the study's purpose, estimation techniques employed included the wavelet approaches (bi-wavelet and partial wavelet), and the wavelet multiple as well as the DCC-GARCH Connectedness approach as robustness. A sample period from January 1997 to August 2021 restricted by consistent data availability was considered. It was discovered that most G8 nations have a comparable relationship between their GDP and CPI. Additionally, significant co-movements between the G8 nations' GDP and CPI straddle crises. Furthermore, the relationship between Russia's GDP and CPI was significantly conditionally influenced by geopolitical risk factors. Own country economic policy uncertainty was the main source of shocks for nations like Canada, France, and the US, whereas, in Germany, Italy, and the UK, Global EPU was a crucial conduit for reducing the lead-lag relationship between GDP and CPI. Outcomes from this study imply that uncertainties pose a more persistent and dynamic challenge to the G8 countries' efforts to achieve sustained economic growth, lessen the negative effects of inflation and deflation, and improve national and regional economic integration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".