Geopolitical risks and inflation: insights across time horizons
Bibliographic record
Abstract
How does geopolitical risk influence inflation? Using monthly data from 1996 to 2023, this study examines the impact of geopolitical risk on inflation across nine advanced economies, the USA, Canada, Belgium, Germany, Spain, France, the UK, Italy, and the Netherlands. A four-step empirical approach is employed: (1) a fixed-effects panel regression model estimates the baseline relationship, (2) a Two-Stage Least Squares (2SLS) approach addresses potential endogeneity, (3) a Panel Vector Autoregressive (Panel-VAR) model explores dynamic interactions, and (4) impulse response functions (IRFs) assess the persistence of geopolitical shocks. The analysis reveals distinct inflationary patterns, showing that while geopolitical risks generally moderate inflation during non-conflict periods, the Russia-Ukraine conflict significantly heightens inflationary pressures over the short, medium, and long term, reflecting prolonged economic disruptions, supply chain issues, and market volatility. These findings offer valuable insights for policymakers, emphasizing the need to integrate geopolitical risks into inflation forecasting and monetary policy to improve accuracy and resilience in the face of global uncertainties.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".