International Co-movements and Business Cycles Synchronization Among Advanced Economies: A SPBVAR Evidence
Bibliographic record
Abstract
This paper provides new empirical insights in order to give a relevant contribution to the more recent literature on international transmission of shocks and on business cycles synchronization across developed economies, with a particular emphasis in the most recent recession and post-crisis consolidation. Interdependence, commonality and heterogeneity in macroeconomic-financial linkages are also identified in order to depict the perplexed nature of modern economies. A time-varying Structural Panel Bayesian Vector Autoregression (SPBVAR) model is developed to deal with model misspecification and unobserved heterogeneity problems when studying multicountry dynamic panels. The results argue for significant synchronization behind a relevant consolidation without delay. Additionally, consolidation is needed to underpin confidence in fiscal solvency at the country level and prevent adverse international externalities. My evidence calls for more integrated macroprudential and financial stability policies. It also shows that, when formulating policies or forecasting, additional transmission channels and economic-institutional issues through which fiscal contractions influence the dynamics of the GDP growth need to be accounted for in muticountry setups.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".