Is Economic Activity in the G7 Synchronized? Common Shocks versus Spillover Effects
Bibliographic record
Abstract
This Paper analyses the co-movement in activity, measured by GDP and industrial production, between the G7 countries for the period 1972-2002. For that purpose, a dynamic factor model is estimated using Kalman Filtering techniques. In addition to separating common and country-specific - idiosyncratic - developments of output, we try to identify the causes underlying the observed co-movement: to what extent is it driven by common shocks and to what extent can cross-country/cross-area spill-over effects account for the observed co-movement? We find that the output developments in G7 countries are driven to a substantial extent by common dynamics. A significant part of the co-movement, especially in the first half of the sample, can be explained by developments in the price of oil, an important and easily identifiable common shock. The analysis suggests that, in addition, area-specific common factors play an important role, separating the sample into a North American (US, Canada) and a continental European (France, Germany, Italy) area, with the UK and Japan being somewhat separate from these areas. We find that developments in the North American factor have a strong lagged impact on the continental European factor, while the reverse is not true. Furthermore, the strength of the cross-area spillovers from America to Europe appears to have become stronger over the sample period, suggesting that international linkages have increased in the process of globalization.
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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".