G10 cross-country connectedness over US growth
Bibliographic record
Abstract
Purpose We extend a classic macroeconomic framework guided by extensive empirical and theoretical literature on growth transmission channels and shock decomposition, with the purpose of measuring the growth spillovers from G-10 countries to the US. Design/methodology/approach We use a time-varying parameter vector autoregressive (TVP-VAR) model with dynamic structures to measure time-varying external spillover effects under different economic conditions, i.e. controlled by a representative set of American macroeconomic variables. Findings Based on our empirical exercise from 1996q3 to 2023q1, we emphasize the roles of France and Russia in the late 1990s as well as the G7 (excluding the US) and Eurozone countries following the pandemic. We also provide insights into the internal transmission channels. Research limitations/implications We find that Germany, Japan and Italy only managed to have a net spillover effect on the US in one or two quarters in 1996 and 1997, while the influence of Canada, China, the UK and India appears to affect American growth between 1996 and 1999. The influences of France and Russia are stronger, as they can impact the American economy for more than 30 quarters. Regarding economic blocs, the G7 (excluding the US) and the Eurozone can impact the US during and after the pandemic. Practical implications Our results on internal pass-through show a relevant role played by the high levels of American debt and interest rates. This finding is relevant and worrying, and it is aligned with literature on the effects of high levels of public indebtedness and inflation after the pandemic, even in developing economies. In this context, according to empirical findings reported by Matos et al. (2024) based on conditional wavelet tools, most relationships between debt and GDP are given by anti-phasic leadership of the debt (0–4-year frequency period), while inflation can lead to growth in the opposite direction (0–8-year frequency period). Social implications This evidence is significant as recent years have reshaped the understanding of power, with several states emerging as new powers. The role of economic blocs after the pandemic supports this viewpoint. To summarize, both the domestic macroeconomic scenario and the geopolitical forces pose challenges to the American economy. Originality/value Our work differs from previous related studies in two aspects. First, unlike most, we use the conditional connectedness approach outlined by Stenfors et al. (2022). Second, we extend a macroeconomic-based growth cycle model instead of a neoclassical approach.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".