International trade fluctuations: Global versus regional factors
Bibliographic record
Abstract
Abstract This paper examines the relative importance of global, regional, country and idiosyncratic factors as well as the determinants that underpin fluctuations in international trade flows across different regions of the world. Our analysis starts by using a Bayesian dynamic latent factor model (BDFM) to simultaneously estimate the four dynamic factors, followed by the application of Bayesian model averaging to identify the variables that explain the shares of variance. Our key findings are: (i) international factors are the most important in explaining fluctuations in international trade, suggesting that the interconnections between economies and policies/shocks at the regional and global level tend to be more important than country‐level factors and (ii) regional integration, particularly when the agreement goes beyond trade in goods, is positively related to the share of the regional factor and inversely related to the importance of the global factor. Furthermore, the regional factor is more important in the case of economically large trade blocks. Overall, our analysis illustrates the usefulness of applying a BDFM model to study the co‐movements of international trade series.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".